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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: New Opportunities for Connected Data
by Ian Robinson Jim Webber 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 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 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 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 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: 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: 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 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: 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
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: 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: 26th International Symposium, GD 2018, Barcelona, Spain, September 26-28, 2018, Proceedings (Lecture Notes in Computer Science #11282)
by Therese Biedl Andreas KerrenThis 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 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 Weili Guan Xuemeng Song Xiaojun Chang Liqiang NieThis 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 Luping Zhou Biao Jie Mingxia LiuThis 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 for Feature Extraction and Classification of Hyperspectral Remote Sensing Images (Intelligent Perception and Information Processing)
by Yao Ding Zhili Zhang Haojie Hu Fang He Shuli Cheng Yijun ZhangThis 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 Network Methods and Applications in Scene Understanding
by Weibin Liu Huaqing Hao Hui Wang Zhiyuan Zou Weiwei XingThe 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 Partitioning
by Charles-Edmond Bichot Patrick SiarryGraph 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.
Graph Polynomials (Discrete Mathematics and Its Applications)
by Yongtang Shi; Matthias Dehmer; Xueliang Li; Ivan GutmanThis book covers both theoretical and practical results for graph polynomials. Graph polynomials have been developed for measuring combinatorial graph invariants and for characterizing graphs. Various problems in pure and applied graph theory or discrete mathematics can be treated and solved efficiently by using graph polynomials. Graph polynomials have been proven useful areas such as discrete mathematics, engineering, information sciences, mathematical chemistry and related disciplines.
Graph-Powered Analytics and Machine Learning with TigerGraph: Driving Business Outcomes with Connected Data
by Victor Lee Phuc Kien Nguyen Alexander ThomasWith the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Phuc Kien Nguyen, and Alexander Thomas present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization.Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learningLearn how graph analytics and machine learning can deliver key business insights and outcomesUse five core categories of graph algorithms to drive advanced analytics and machine learningDeliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizenDiscover insights from connected data through machine learning and advanced analytics
Graph-Powered Machine Learning
by Alessandro NegroUpgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You&’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro&’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you&’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs
Graph-related Optimization and Decision Support Systems (Wiley-iste Ser.)
by Saoussen Krichen Jouhaina ChaouachiConstrained optimization is a challenging branch of operations research that aims to create a model which has a wide range of applications in the supply chain, telecommunications and medical fields. As the problem structure is split into two main components, the objective is to accomplish the feasible set framed by the system constraints. The aim of this book is expose optimization problems that can be expressed as graphs, by detailing, for each studied problem, the set of nodes and the set of edges. This graph modeling is an incentive for designing a platform that integrates all optimization components in order to output the best solution regarding the parameters' tuning. The authors propose in their analysis, for optimization problems, to provide their graphical modeling and mathematical formulation and expose some of their variants. As a solution approaches, an optimizer can be the most promising direction for limited-size instances. For large problem instances, approximate algorithms are the most appropriate way for generating high quality solutions. The authors thus propose, for each studied problem, a greedy algorithm as a problem-specific heuristic and a genetic algorithm as a metaheuristic.