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Applied Graph Data Science
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
R 5,297.00
Graph Data Science with Neo4j
Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learningPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExtract meaningful information from graph data with Neo4j's latest version 5Use Graph Algorithms into a regular Machine Learning pipeline in PythonLearn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.Book DescriptionNeo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance.Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline.By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.What you will learnUse the Cypher query language to query graph databases such as Neo4jBuild graph datasets from your own data and public knowledge graphsMake graph-specific predictions such as link predictionExplore the latest version of Neo4j to build a graph data science pipelineRun a scikit-learn prediction algorithm with graph dataTrain a predictive embedding algorithm in GDS and manage the model storeWho this book is forIf you’re a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you’ll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.
R 1,596.00
Applied Deep Learning on Graphs
Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domainsKey FeaturesExplore graph data in real-world systems and leverage graph learning for impactful business resultsDive into popular and specialized deep neural architectures like graph convolutional and attention networksLearn how to build scalable and productionizable graph learning solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWith their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs).This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision.By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies.What you will learnDiscover how to extract business value through a graph-centric approachDevelop a basic understanding of learning graph attributes using machine learningIdentify the limitations of traditional deep learning with graph data and explore specialized graph-based architecturesUnderstand industry applications of graph deep learning, including recommender systems and NLPIdentify and overcome challenges in production such as scalability and interpretabilityPerform node classification and link prediction using PyTorch GeometricWho this book is forFor data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.
R 1,693.00
Graph Machine Learning
Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric, and DGLKey FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine learningBuild and scale graph-powered ML applications effectivelyDeploy and scale your application seamlesslyWho this book is forThis book is for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.
R 1,791.00
R 2,838.51
Scaling Graph Learning for the Enterprise
Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process
R 1,824.00
Applied Data Science
The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.
R 8,123.41
Demystifying Graph Data Science
With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data. Supported by query languages, databases, algorithms, platforms, analytics methods and machine and deep learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships. Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph data science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data. This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, artificial intelligence/machine and deep learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.
R 4,559.00
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15-19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks.
R 3,558.72
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows:Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
R 2,226.07
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions.The volumes are organized in topical sections as follows:Research Track:Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications.Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety.Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics.Applied Data Science Track:Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation.Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
R 3,550.49
Graph Neural Networks: Essentials and Use Cases
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals--and how they complement advances in generative, agentic, and physical AI--this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data--complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node's local neighborhood by aggregating information from adjacent nodes, updating the node's representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
R 8,661.69
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows:Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
R 3,942.73
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
R 2,768.68
Knowledge Graphs and LLMs in Action
Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights. In Knowledge Graphs and LLMs in Action you will learn how to: Model knowledge graphs with an iterative top-down approach based in business needsCreate a knowledge graph starting from ontologies, taxonomies, and structured dataUse machine learning algorithms to hone and complete your graphsBuild knowledge graphs from unstructured text data sourcesReason on the knowledge graph and apply machine learning algorithms Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs and LLMs in Action, you'll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You'll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance. About the technology: Knowledge graphs represent a network of real-world entities—from people and places to genes and proteins—and model the relationships between them. KGs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information that's vital for human knowledge. They're poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more.
R 2,214.00
Applied Data Science in Tourism
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau
R 4,247.33
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows:Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: Robustness; Time Series; Transfer and Multitask Learning.Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
R 3,965.37
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions.The volumes are organized in topical sections as follows:Research Track:Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications.Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety.Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics.Applied Data Science Track:Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation.Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
R 3,556.22
Applied Social Network Analysis With R
Understanding the social relations within the fields of business and economics is vital for the promotion of success within a certain organization. Analytics and statistics have taken a prominent role in marketing and management practices as professionals are constantly searching for a competitive advantage. Converging these technological tools with traditional methods of business relations is a trending area of research. Applied Social Network Analysis With R: Emerging Research and Opportunities is an essential reference source that materializes and analyzes the issue of structure in terms of its effects on human societies and the state of the individuals in these communities. Even though the theme of the book is business-oriented, an approach underlining and strengthening the ties of this field of study with social sciences for further development is adopted throughout. Therefore, the knowledge presented is valid for analyzing not only the organization of the business world but also for the organization of any given community. Featuring research on topics such as network visualization, graph theory, and micro-dynamics, this book is ideally designed for researchers, practitioners, business professionals, managers, programmers, academicians, and students seeking coverage on analyzing social and business networks using modern methods of statistics, programming, and data sets.
R 5,438.53
Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track
This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15-19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks.
R 3,564.47
Exponential Random Graph Models for Social Networks
Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.
R 1,772.93
Graph-Theoretic Concepts in Computer Science
The aim of this workshop series is to contribute to integration in computer science by applying graph-theoretic concepts. Commonalities between various fields of specialization in computer science may be detected by applying graph-theoretic concepts. The workshops are unusual in that they combine theoretical aspects with practice and applications. Applications dealt with in this volume include the use of graph-theoretic concepts in distributed and parallel computation, VLSI, CAD, software engineering, computer graphics, data structures, and computational geometry.
R 1,570.92
Exponential Random Graph Models for Social Networks
Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.
R 3,878.44
Data Science: Foundations and Applications
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
R 3,542.57
Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,591.65
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,588.56
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,587.83
Applied Categorical Data Analysis and Translational Research
An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy-to-follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real-world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book's examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.
R 4,460.00
Applied Graph Theory: An Introduction With Graph Optimization And Algebraic Graph Theory
This book serves as an introduction to graph theory and its applications. It is intended for a senior undergraduate course in graph theory but is also appropriate for beginning graduate students in science or engineering. The book presents a rigorous (proof-based) introduction to graph theory while also discussing applications of the results for solving real-world problems of interest. The book is divided into four parts. Part 1 covers the combinatorial aspects of graph theory including a discussion of common vocabulary, a discussion of vertex and edge cuts, Eulerian tours, Hamiltonian paths and a characterization of trees. This leads to Part 2, which discusses common combinatorial optimization problems. Spanning trees, shortest path problems and matroids are all discussed, as are maximum flow problems. Part 2 ends with a discussion of graph coloring and a proof of the NP-completeness of the coloring problem. Part 3 introduces the reader to algebraic graph theory, and focuses on Markov chains, centrality computation (e.g., eigenvector centrality and page rank), as well as spectral graph clustering and the graph Laplacian. Part 4 contains additional material on linear programming, which is used to provide an alternative analysis of the maximum flow problem. Two appendices containing prerequisite material on linear algebra and probability theory are also provided.
R 3,590.00
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. The authors believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
R 9,319.59
Data Science: Foundations and Applications
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
R 3,537.59
Algebraic Foundations for Applied Topology and Data Analysis
This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user’s guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.
R 2,354.58
Graph Data Management
Graph Data Management: Techniques and Applications is a central reference source for different data management techniques for graph data structures and their application. This book discusses graphs for modeling complex structured and schemaless data from the Semantic Web, social networks, protein networks, chemical compounds, and multimedia databases and offers essential research for academics working in the interdisciplinary domains of databases, data mining, and multimedia technology.
R 7,603.66
Elements of Network Science
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed.The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online.The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.
R 5,197.96
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. The authors believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
R 7,326.00
Getting Started with the Graph Query Language (GQL)
Learn how to build and query graph databases with this first comprehensive guide to ISO-standard GQL featuring 50+ hands-on examples and a real-world case study that will change the way you work with connected dataKey FeaturesGo beyond theory and apply key concepts and syntax through interactive tutorials and practical examples via the GQL PlaygroundLeverage advanced features of GQL to manipulate graph data efficientlyExplore GQL applications in data analytics and discover how to leverage graph knowledge in real-world scenariosPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Query Language is becoming the go-to standard for graph databases, especially with its support for interconnected analytics and GenAI capabilities. This book comes from a team of industry veterans who know exactly how to break down the fundamental GQL concepts, graph terms, definitions, catalog systems, and everything that matters in actual work.You’ll get to grips with graph data types, value expressions, graph matching patterns, and modifying statements through practical GQL examples. With access to the GQL Playground via Ultipa Graph, you’ll get hands-on experience querying and manipulating graph data. Once you've got the basics down, you’ll tackle advanced GQL topics such as path modes, complex path matching patterns, shortest path queries, composite statements, session and transaction commands, and procedures. You’ll also learn to create extensions and understand the design of graph databases to solve industry issues. The authors cover techniques like property graphs to help you optimize your graph queries and offer insights into the future of GQL and graph technology.By the end of this book, you’ll confidently query and update graph data, run graph algorithms, and create visualizations. Plus, you’ll apply your learnings to a real-world use case of money flow analysis for assessing bank client behaviors and detecting transaction risks.What you will learnExperiment with GQL syntax on GQL Playground, including MATCH, RETURN, INSERT, UPDATE, and DELETEWork with operators, functions, and variables in an organized fashionBecome familiar with complex topics such as varying path matching modes, repeated variables, shortest path, procedures, and transactionsEnhance execution speed through indexing or caching systemsUnderstand how to manage access control effectivelyTackle real-world issues with a case study focused on money transaction analyticsWho this book is forThis book is for graph database developers, DBAs, programmers, data engineers, and analysts who want to learn the new graph database standard GQL. A basic understanding of graph and relational databases, data models, knowledge of SQL basics, and programming will make the content easy to grasp. While it is designed to be accessible even if you don’t have a background in graph theory, familiarity with concepts like nodes, edges, relationships, and the distinction between directed and un
R 1,693.00
Applied Statistics for Social and Management Sciences
This book addresses the application of statistical techniques and methods across a wide range of disciplines. While its main focus is on the application of statistical methods, theoretical aspects are also provided as fundamental background information. It offers a systematic interpretation of results often discovered in general descriptions of methods and techniques such as linear and non-linear regression. SPSS is also used in all the application aspects. The presentation of data in the form of tables and graphs throughout the book not only guides users, but also explains the statistical application and assists readers in interpreting important features. The analysis of statistical data is presented consistently throughout the text. Academic researchers, practitioners and other users who work with statistical data will benefit from reading Applied Statistics for Social and Management Sciences.
R 3,593.46
Data Science with Semantic Technologies
DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.
R 7,327.00
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,206.91
Linear Algebra, Data Science, and Machine Learning
This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.
R 2,931.00
Data Science for Fake News
This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools.The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news.The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
R 5,692.39
Graph Learning Techniques
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
R 2,940.38
Cooperative and Graph Signal Processing
Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings.
R 3,678.00
Applied Statistics and Data Science
This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.
R 5,814.55
Advancing Recommender Systems with Graph Convolutional Networks
This book systematically examines scalability and effectiveness challenges related to the application of graph convolutional networks (GCNs) in recommender systems. By effectively modeling graph structures, GCNs excel in capturing high-order relationships between users and items, enabling the creation of enriched and expressive representations.The book focuses on two overarching problem categories: the first area deals with problems specific to GCN-based recommendation models, including over-smoothing, noisy neighboring nodes, and interpretability limitations. The second one encompasses broader challenges in recommendation systems that GCN-based methods are particularly well-suited to address as the attribute missing problem or feature misalignment. Through rigorous exploration of these challenges, this book presents innovative GCN-based solutions to push the boundaries of recommender system design. To this end, techniques such as interest-aware message-passing strategy, cluster-based collaborative filtering, semantic aspects extraction, attribute-aware attention mechanisms, and light graph transformer are presented.Each chapter combines theoretical insights with practical implementations and experimental validation, offering a comprehensive resource for researchers, advanced professionals, and graduate students alike.
R 4,762.00
Applied Geospatial Data Science with Python
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important conceptsKey FeaturesLearn how to integrate spatial data and spatial thinking into traditional data science workflowsDevelop a spatial perspective and learn to avoid common pitfalls along the wayGain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expandedBook DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learnUnderstand the fundamentals needed to work with geospatial dataTransition from tabular to geo-enabled data in your workflowsDevelop an introductory portfolio of spatial data science work using PythonGain hands-on skills with case studies relevant to different industriesDiscover best practices focusing on geospatial data to bring a positive change in your environmentExplore solving use cases, such as traveling salesperson and vehicle routing problemsWho this book is forThis book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
R 1,889.00
Network Models for Data Science
This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.
R 3,210.00
Applied Data Science with Python and Jupyter
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
R 1,140.00
