This book contains Volume 6 of the Journal of Graph Algorithms and Applications (JGAA) . JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. Graph Algorithms and Applications 3 presents contributions from prominent authors and includes selected papers from the Symposium on Graph Drawing (1999 and 2000). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications. Contents: Triangle-Free Planar Graphs and Segment Intersection Graphs (N de Castro et al.); Traversing Directed Eulerian Mazes (S Bhatt et al.); A Fast Multi-Scale Method for Drawing Large Graphs (D Harel & Y Koren); GRIP: Graph Drawing with Intelligent Placement (P Gajer & S G Kobourov); Graph Drawing in Motion (C Friedrich & P Eades); A 6-Regular Torus Graph Family with Applications to Cellular and Interconnection Networks (M Iridon & D W Matula); and other papers. Readership: Researchers and practitioners in theoretical computer science, computer engineering, and combinatorics and graph theory.

Revised throughout Includes new chapters on the network simplex algorithm and a section on the five color theorem Recent developments are discussed

Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

This book contains Volume 7 of the Journal of Graph Algorithms and Applications (JGAA). JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design.Graph Algorithms and Applications 4 presents contributions from prominent authors and includes selected papers from (a) the Seventh International Workshop on Algorithms and Data Structures (WADS 2001) and (b) the 2001 Symposium on Graph Drawing (GD 2001). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications.

This introductory book treats algorithmic graph theory specifically for programmers. It explores some key ideas and basic algorithms in this large and rapidly growing field, and contains high-level and language-independent descriptions of methods and algorithms on trees, the most important type of graphs in programming and informatics. Readers are assumed to be familiar with the basics of graph theory, and programming. Audience: This volume will be of interest to researchers and specialists in programming, software engineering, data structure and information retrieval, and to mathematicians whose work involves algorithms, combinatorics, graph theory, operations research, and discrete optimization. The book can also be recommended as a text for graduate courses in computer science, electronics, telecommunications, and control engineering.

Because of its portability and platform-independence, Java is the ideal computer programming language to use when working on graph algorithms and other mathematical programming problems. Collecting some of the most popular graph algorithms and optimization procedures, A Java Library of Graph Algorithms and Optimization provides the source code for

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark