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Graph Coloring: From Games to Deterministic and Quantum Approaches (Advances in Metaheuristics)
by Maurice ClercThis book explores the problem of minimal valid graph coloring, first in the form of games and then of resolution algorithms. Emphasis is placed on deterministic, guaranteed and non-guaranteed methods. Stochastic methods are then just mentioned because they are already widely described in previous publications.The study then details a general quantum algorithm of polynomial complexity. A final chapter provides elements of reflection on diplomatic algorithms that, for the problem of coloring under resource constraints, seek a compromise minimizing frustrations. The appendix includes some mathematical additions and the source codes of the main algorithms presented, in particular the one of the quantum method.
Graph Data Mining: Algorithm, Security and Application (Big Data Management)
by Qi Xuan Zhongyuan Ruan Yong MinGraph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.
Graph Data Processing with Cypher: A practical guide to building graph traversal queries using the Cypher syntax on Neo4j
by Ravindranatha AnthapuGet acquainted with Cypher in a guided manner quickly and learn how to query the graph databases with efficient and performant queriesKey FeaturesWork with Cypher syntax and semantics while building graph traversal queriesGet up and running with advanced Cypher concepts like List, Maps, OPTIONAL MATCHMaster best practices in writing effective queries leveraging data modeling and patternsBook DescriptionWhile it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way.This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You'll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users.After having worked through this book, you'll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.What you will learnWrite Cypher queries from basic to advanced levelMap the source data to the graph data model in an iterative fashionLoad the data into a graph using LOAD CSV, APOC, and client driversMap the business questions to graph queries effectivelyIdentify query performance issues and fix themExtend capabilities of Cypher using APOC utilitiesWork with graph visualization tools like Bloom and BrowserWho this book is forThis book is targeted at Database Administrator, Database Developers, Graph Database Developers, and Graph Database Architects. This book will also help someone migrate from a DBA role to a graph data engineer or data scientistIf you are working with graph databases and need to learn Cypher, or are a basic Cypher developer who wants to get better at data modeling and tuning queries to build performant Cypher queries, then this is the book for you.
Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project
by Estelle ScifoSupercharge 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.
Graph Databases in Action: Examples in Gremlin (In Action Ser.)
by Josh Perryman Dave BechbergerGraph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.Summary Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Isolated data is a thing of the past! Now, data is connected, and graph databases—like Amazon Neptune, Microsoft Cosmos DB, and Neo4j—are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value. About the book Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization. What's inside Graph databases vs. relational databases Systematic graph data modeling Querying and navigating a graph Graph patterns Pitfalls and antipatterns About the reader For software developers. No experience with graph databases required. About the author Dave Bechberger and Josh Perryman have decades of experience building complex data-driven systems and have worked with graph databases since 2014. Table of Contents PART 1 - GETTING STARTED WITH GRAPH DATABASES 1 Introduction to graphs 2 Graph data modeling 3 Running basic and recursive traversals 4 Pathfinding traversals and mutating graphs 5 Formatting results 6 Developing an application PART 2 - BUILDING ON GRAPH DATABASES 7 Advanced data modeling techniques 8 Building traversals using known walks 9 Working with subgraphs PART 3 - MOVING BEYOND THE BASICS 10 Performance, pitfalls, and anti-patterns 11 What's next: Graph analytics, machine learning, and resources
Graph Databases: Applications on Social Media Analytics and Smart Cities
by Christos TjortjisWith social media producing such huge amounts of data, the importance of gathering this rich data, often called "the digital gold rush", processing it and retrieving information is vital. This practical book combines various state-of-the-art tools, technologies and techniques to help us understand Social Media Analytics, Data Mining and Graph Databases, and how to better utilize their potential. Graph Databases: Applications on Social Media Analytics and Smart Cities reviews social media analytics with examples using real-world data. It describes data mining tools for optimal information retrieval; how to crawl and mine data from Twitter; and the advantages of Graph Databases. The book is meant for students, academicians, developers and simple general users involved with Data Science and Graph Databases to understand the notions, concepts, techniques, and tools necessary to extract data from social media, which will aid in better information retrieval, management and prediction.
Graph Databases: New Opportunities for Connected Data
by Jim Webber Ian Robinson Emil EifremDiscover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information
Graph Drawing and Network Visualization
by Emilio Di Giacomo Anna LubiwThis book constitutes the proceedings of the 23rdInternational Symposium on Graph Drawing and Network Visualization, GD 2015,held in Los Angeles, Ca, USA, in September 2015. The 35 full papers presented together with 7 short papersand 8 posters in this volume were carefully reviewed and selected from 77submissions. Graph Drawing is concerned with the geometric representation ofgraphs and constitutes the algorithmic core of Network Visualization. GraphDrawing and Network Visualization are motivated by applications where it iscrucial to visually analyze and interact with relational datasets. Examples ofsuch application areas include social sciences, Internet and Web computing,information systems, computational biology, networking, VLSI circuit design,and software engineering. This year the Steering Committee of GD decided to extendthe name of the conference from the "International Symposium on GraphDrawing" to the "International Symposium on Graph Drawing and NetworkVisualization" in order to better emphasize the dual focus of theconference on combinatorial and algorithmic aspects as well as the design ofnetwork visualization systems and interfaces.
Graph Drawing and Network Visualization
by Yifan Hu Martin NöllenburgThis book constitutes revised selected papers from the 24th International Symposium on Graph Drawing and Network Visualization, GD 2016, held in Athens, Greece, in September 2016. The 45 papers presented in this volume were carefully reviewed and selected from 99 submissions. They were organized in topical sections named: large graphs and clutter avoidance; clustered graphs; planar graphs, layered and tree drawings; visibility representations; beyond planarity; crossing minimization and crossing numbers; topological graph theory; special graph embeddings; dynamic graphs, contest report.
Graph Drawing and Network Visualization: 25th International Symposium, GD 2017, Boston, MA, USA, September 25-27, 2017, Revised Selected Papers (Lecture Notes in Computer Science #10692)
by Fabrizio Frati Kwan-Liu MaThis book constitutes revised selected papers from the 25th International Symposium on Graph Drawing and Network Visualization, GD 2017, held in Boston, MA, USA, in September 2017.The 34 full and 9 short papers presented in this volume were carefully reviewed and selected from 87 submissions. Also included in this book are 2 abstracts of keynote presentations, 16 poster abstracts, and 1 contest report. The papers are organized in topical sections named: straight-line representations; obstacles and visibility; topological graph theory; orthogonal representations and book embeddings; evaluations; tree drawings; graph layout designs; point-set embeddings; special representations; and beyond planarity.
Graph Drawing and Network Visualization: 26th International Symposium, GD 2018, Barcelona, Spain, September 26-28, 2018, Proceedings (Lecture Notes in Computer Science #11282)
by Andreas Kerren Therese BiedlThis book constitutes the refereed proceedings of the 26th International Symposium on Graph Drawing and Network Visualization, GD 2018, held in Barcelona, Spain, in September 2018. The 41 full papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named: planarity variants; upward drawings; RAC drawings; orders; crossings; crossing angles; contact representations; specialized graphs and trees; partially fixed drawings, experiments; orthogonal drawings; realizability; and miscellaneous. The book also contains one invited talk in full paper length and the Graph Drawing contest report.
Graph Drawing and Network Visualization: 27th International Symposium, GD 2019, Prague, Czech Republic, September 17–20, 2019, Proceedings (Lecture Notes in Computer Science #11904)
by Daniel Archambault Csaba D. TóthThis book constitutes the refereed proceedings of the 27th International Symposium on Graph Drawing and Network Visualization, GD 2019, held in Prague, Czech Republic, in September 2019.The 42 papers and 12 posters presented in this volume were carefully reviewed and selected from 113 submissions. They were organized into the following topical sections: Cartograms and Intersection Graphs, Geometric Graph Theory, Clustering, Quality Metrics, Arrangements, A Low Number of Crossings, Best Paper in Track 1, Morphing and Planarity, Parameterized Complexity, Collinearities, Topological Graph Theory, Best Paper in Track 2, Level Planarity, Graph Drawing Contest Report, and Poster Abstracts.
Graph Drawing and Network Visualization: 28th International Symposium, GD 2020, Vancouver, BC, Canada, September 16–18, 2020, Revised Selected Papers (Lecture Notes in Computer Science #12590)
by David Auber Pavel ValtrThis book constitutes the refereed proceedings of the 28th International Symposium on Graph Drawing and Network Visualization, GD 2020, which was held during September 16-18, 2020. The conference was planned to take place in Vancouver, Canada, but changed to an online format due to the COVID-19 pandemic. The 29 full and 9 short papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections named: gradient descent and queue layouts; drawing tree-like graphs, visualization, and special drawings of elementary graphs; restricted drawings of special graph classes; orthogonality; topological constraints; crossings, k-planar graphs; planarity; graphs drawing contest.
Graph Drawing and Network Visualization: 29th International Symposium, GD 2021, Tübingen, Germany, September 14–17, 2021, Revised Selected Papers (Lecture Notes in Computer Science #12868)
by Helen C. Purchase Ignaz RutterThis book constitutes the proceedings of the 28th International Symposium on Graph Drawing and Network Visualization, GD 2021, which was held in Tübingen, Germany, during September 14-17, 2021.The 23 full papers and 5 short papers presented in these proceedings were carefully reviewed and selected from 74 submissions. The abstracts of 13 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: Best Paper (Track 1: Combinatorial and Algorithmic Aspects); Best Paper (Track 2: Experimental, Applied, and Network Visualization Aspects); Crossing Minimization and Beyond-Planarity; Morphing and Graph Abstraction; Geometric Constraints; Topological and Upward Drawings; Linear Layouts; Contact and Visibility Representations; Geometric Aspects in Graph Drawing; AI applications; and Graph Drawing Contest Report.
Graph Drawing and Network Visualization: 30th International Symposium, GD 2022, Tokyo, Japan, September 13–16, 2022, Revised Selected Papers (Lecture Notes in Computer Science #13764)
by Patrizio Angelini Reinhard Von HanxledenThis book constitutes the proceedings of the 30th International Symposium on Graph Drawing and Network Visualization, GD 2022, held in Tokyo, Japan, during September 13-16, 2022. The 25 full papers, 7 short papers, presented together with 2 invited talks, one report on graph drawing contest, and one obituary in these proceedings were carefully reviewed and selected from 70 submissions. The abstracts of 5 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: properties of drawings of complete graphs; stress-based visualizations of graphs; planar and orthogonal drawings; drawings and properties of directed graphs; beyond planarity; dynamic graph visualization; linear layouts; and contact and visibility graph representations.
Graph Drawing and Network Visualization: 31st International Symposium, GD 2023, Isola delle Femmine, Palermo, Italy, September 20–22, 2023, Revised Selected Papers, Part I (Lecture Notes in Computer Science #14465)
by Michael A. Bekos Markus ChimaniThis two-volume set LNCS 14465-14466 constitutes the proceedings of the 31st International Symposium on Graph Drawing and Network Visualization, GD 2023, held in Isola delle Femmine, Palermo, Italy, in September 2023. The 31 full papers, 7 short papers, presented together with 2 invited talks, and one contest report, were thoroughly reviewed and selected from the 100 submissions. The abstracts of 11 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: beyond planarity; crossing numbers; linear layouts; geometric aspects; visualization challenges; graph representations; graph decompositions; topological aspects; parameterized complexity for drawings; planar graphs; frameworks; algorithmics.
Graph Drawing and Network Visualization: 31st International Symposium, GD 2023, Isola delle Femmine, Palermo, Italy, September 20–22, 2023, Revised Selected Papers, Part II (Lecture Notes in Computer Science #14466)
by Michael A. Bekos Markus ChimaniThis two-volume set LNCS 14465-14466 constitutes the proceedings of the 31st International Symposium on Graph Drawing and Network Visualization, GD 2023, held in Isola delle Femmine, Palermo, Italy, in September 2023. The 31 full papers, 7 short papers, presented together with 2 invited talks, and one contest report, were thoroughly reviewed and selected from the 100 submissions. The abstracts of 11 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: beyond planarity; crossing numbers; linear layouts; geometric aspects; visualization challenges; graph representations; graph decompositions; topological aspects; parameterized complexity for drawings; planar graphs; frameworks; algorithmics.
Graph Learning and Network Science for Natural Language Processing (Computational Intelligence Techniques)
by Muskan Garg, Amit Kumar Gupta and Rajesh PrasadAdvances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: -Presents a comprehensive study of the interdisciplinary graphical approach to NLP -Covers recent computational intelligence techniques for graph-based neural network models -Discusses advances in random walk-based techniques, semantic webs, and lexical networks -Explores recent research into NLP for graph-based streaming data -Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
Graph Learning for Fashion Compatibility Modeling (Synthesis Lectures on Information Concepts, Retrieval, and Services)
by Liqiang Nie Xuemeng Song Weili Guan Xiaojun ChangThis book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.
Graph Learning in Medical Imaging: First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (Lecture Notes in Computer Science #11849)
by Daoqiang Zhang Mingxia Liu Luping Zhou Biao JieThis book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
by Claudio Stamile Aldo Marzullo Enrico DeusebioBuild machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relationship between nodes in order to make better business decisionsApply graph-based machine learning methods to solve real-life problemsBook DescriptionGraph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.What you will learnWrite Python scripts to extract features from graphsDistinguish between the main graph representation learning techniquesBecome well-versed with extracting data from social networks, financial transaction systems, and moreImplement the main unsupervised and supervised graph embedding techniquesGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and moreDeploy and scale out your application seamlesslyWho this book is forThis book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance. The book will also be useful for data scientists and machine learning developers who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.
Graph Neural Network Methods and Applications in Scene Understanding
by Weibin Liu Weiwei Xing Hui Wang Huaqing Hao Zhiyuan ZouThe book focuses on graph neural network methods and applications for scene understanding. Graph Neural Network is an important method for graph-structured data processing, which has strong capability of graph data learning and structural feature extraction. Scene understanding is one of the research focuses in computer vision and image processing, which realizes semantic segmentation and object recognition of image or video. In this book, the algorithm, system design and performance evaluation of scene understanding based on graph neural networks have been studied. First, the book elaborates the background and basic concepts of graph neural network and scene understanding, then introduces the operation mechanism and key methodological foundations of graph neural network. The book then comprehensively explores the implementation and architectural design of graph neural networks for scene understanding tasks, including scene parsing, human parsing, and video object segmentation. The aim of this book is to provide timely coverage of the latest advances and developments in graph neural networks and their applications to scene understanding, particularly for readers interested in research and technological innovation in machine learning, graph neural networks and computer vision. Features of the book include self-supervised feature fusion based graph convolutional network is designed for scene parsing, structure-property based graph representation learning is developed for human parsing, dynamic graph convolutional network based on multi-label learning is designed for human parsing, and graph construction and graph neural network with transformer are proposed for video object segmentation.
Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images (Intelligent Perception and Information Processing)
by Yijun Zhang Yao Ding Zhili Zhang Haojie Hu Fang He Shuli ChengThis book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.
Graph Neural Networks in Action (In Action)
by Keita Broadwater Namid StillmanA hands-on guide to powerful graph-based deep learning models.Graph Neural Networks in Action teaches you to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba&’s GraphScope for training at scale. In Graph Neural Networks in Action, you will learn how to: • Train and deploy a graph neural network • Generate node embeddings • Use GNNs at scale for very large datasets • Build a graph data pipeline • Create a graph data schema • Understand the taxonomy of GNNs • Manipulate graph data with NetworkX In Graph Neural Networks in Action you&’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. Foreword by Matthias Fey. About the technology Graphs are a natural way to model the relationships and hierarchies of real-world data. Graph neural networks (GNNs) optimize deep learning for highly-connected data such as in recommendation engines and social networks, along with specialized applications like molecular modeling for drug discovery. About the book Graph Neural Networks in Action teaches you how to analyze and make predictions on data structured as graphs. You&’ll work with graph convolutional networks, attention networks, and auto-encoders to take on tasks like node classification, link prediction, working with temporal data, and object classification. Along the way, you&’ll learn the best methods for training and deploying GNNs at scale—all clearly illustrated with well-annotated Python code! What's inside • Train and deploy a graph neural network • Generate node embeddings • Use GNNs for very large datasets • Build a graph data pipeline About the reader For Python programmers familiar with machine learning and the basics of deep learning. About the author Keita Broadwater, PhD, MBA is a seasoned machine learning engineer. Namid Stillman, PhD is a research scientist and machine learning engineer with more than 20 peer-reviewed publications. Table of Contents Part 1 1 Discovering graph neural networks 2 Graph embeddings Part 2 3 Graph convolutional networks and GraphSAGE 4 Graph attention networks 5 Graph autoencoders Part 3 6 Dynamic graphs: Spatiotemporal GNNs 7 Learning and inference at scale 8 Considerations for GNN projects A Discovering graphs B Installing and configuring PyTorch Geometric
Graph Partitioning
by Patrick Siarry Charles-Edmond BichotGraph partitioning is a theoretical subject with applications in many areas, principally: numerical analysis, programs mapping onto parallel architectures, image segmentation, VLSI design. During the last 40 years, the literature has strongly increased and big improvements have been made. This book brings together the knowledge accumulated during many years to extract both theoretical foundations of graph partitioning and its main applications.