Download or read book Graph Theory Computational Intelligence and Thought written by Marina Lipshteyn and published by Springer Science & Business Media. This book was released on 2009-07-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Martin Charles Golumbic has been making seminal contributions to algorithmic graph theory and artificial intelligence throughout his career. He is universally admired as a long-standing pillar of the discipline of computer science. He has contributed to the development of fundamental research in artificial intelligence in the area of complexity and spatial-temporal reasoning as well as in the area of compiler optimization. Golumbic's work in graph theory led to the study of new perfect graph families such as tolerance graphs, which generalize the classical graph notions of interval graph and comparability graph. He is credited with introducing the systematic study of algorithmic aspects in intersection graph theory, and initiated research on new structured families of graphs including the edge intersection graphs of paths in trees (EPT) and trivially perfect graphs. Golumbic is currently the founder and director of the Caesarea Edmond Benjamin de Rothschild Institute for Interdisciplinary Applications of Computer Science at the University of Haifa. He also served as chairman of the Israeli Association of Artificial Intelligence (1998-2004), and founded and chaired numerous international symposia in discrete mathematics and in the foundations of artificial intelligence. This Festschrift volume, published in honor of Martin Charles Golumbic on the occasion of his 60th birthday, contains 20 papers, written by graduate students, research collaborators, and computer science colleagues, who gathered at a conference on subjects related to Martin Golumbic's manifold contributions in the field of algorithmic graph theory and artificial intelligence, held in Jerusalem, Tiberias and Haifa, Israel in September 2008.
Download or read book Graph Theory Computational Intelligence and Thought written by Marina Lipshteyn and published by Springer. This book was released on 2009-07-20 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Martin Charles Golumbic has been making seminal contributions to algorithmic graph theory and artificial intelligence throughout his career. He is universally admired as a long-standing pillar of the discipline of computer science. He has contributed to the development of fundamental research in artificial intelligence in the area of complexity and spatial-temporal reasoning as well as in the area of compiler optimization. Golumbic's work in graph theory led to the study of new perfect graph families such as tolerance graphs, which generalize the classical graph notions of interval graph and comparability graph. He is credited with introducing the systematic study of algorithmic aspects in intersection graph theory, and initiated research on new structured families of graphs including the edge intersection graphs of paths in trees (EPT) and trivially perfect graphs. Golumbic is currently the founder and director of the Caesarea Edmond Benjamin de Rothschild Institute for Interdisciplinary Applications of Computer Science at the University of Haifa. He also served as chairman of the Israeli Association of Artificial Intelligence (1998-2004), and founded and chaired numerous international symposia in discrete mathematics and in the foundations of artificial intelligence. This Festschrift volume, published in honor of Martin Charles Golumbic on the occasion of his 60th birthday, contains 20 papers, written by graduate students, research collaborators, and computer science colleagues, who gathered at a conference on subjects related to Martin Golumbic's manifold contributions in the field of algorithmic graph theory and artificial intelligence, held in Jerusalem, Tiberias and Haifa, Israel in September 2008.
Download or read book Graph Theory with Applications to Engineering and Computer Science written by Narsingh Deo and published by PHI Learning Pvt. Ltd.. This book was released on 1974 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of its inherent simplicity, graph theory has a wide range of applications in engineering, and in physical sciences. It has of course uses in social sciences, in linguistics and in numerous other areas. In fact, a graph can be used to represent almost any physical situation involving discrete objects and the relationship among them. Now with the solutions to engineering and other problems becoming so complex leading to larger graphs, it is virtually difficult to analyze without the use of computers. This book is recommended in IIT Kharagpur, West Bengal for B.Tech Computer Science, NIT Arunachal Pradesh, NIT Nagaland, NIT Agartala, NIT Silchar, Gauhati University, Dibrugarh University, North Eastern Regional Institute of Management, Assam Engineering College, West Bengal Univerity of Technology (WBUT) for B.Tech, M.Tech Computer Science, University of Burdwan, West Bengal for B.Tech. Computer Science, Jadavpur University, West Bengal for M.Sc. Computer Science, Kalyani College of Engineering, West Bengal for B.Tech. Computer Science. Key Features: This book provides a rigorous yet informal treatment of graph theory with an emphasis on computational aspects of graph theory and graph-theoretic algorithms. Numerous applications to actual engineering problems are incorpo-rated with software design and optimization topics.
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Download or read book The Practitioner s Guide to Graph Data written by Denise Gosnell and published by "O'Reilly Media, Inc.". This book was released on 2020-03-20 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Download or read book Theory and Applications of Models of Computation written by Jan Kratochvil and published by Springer Science & Business Media. This book was released on 2010-05-20 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Theory and Applications of Models of Computation, TAMC 2010, held in Prague, Czech Republic, in June 2010. The 35 revised full papers presented together with 5 contributions of special sessions as well as 2 plenary talks were carefully reviewed and selected from 76 submissions. The papers address the three main themes of the conference which were computability, complexity, and algorithms and present current research in these fields with aspects to theoretical computer science, algorithmic mathematics, and applications to the physical sciences.
Download or read book POSSIBILITY THINKING EXPLORATIONS IN LOGIC AND THOUGHT written by JUSTIN COSLOR and published by Lulu.com. This book was released on 2015-11-03 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book pertains largely to the topic of Epistemology; i.e. Mathematics, Computer Science, Artificial Intelligence, Knowledge Representation, Context, and Formal Systems. The author Justin M. Coslor is currently the Minister of Intelligence of the Formalized Research Database: Cluster, Study, and Apply (FRD: CSA); an ongoing Artificial Intelligence ontological systems integration effort primarily designed, put together, and implemented by the creative and technical genius Andrew J. Dougherty. See HTTP: //FRDCSA.ORG and HTTP: //PICFORM.ORG (Note: there are some errors in this text, and other human errors. JMC) POSSIBILITY THINKING: EXPLORATIONS IN LOGIC AND THOUGHT (SECOND EDITION) ISBN: 9781257808519 Printed in Paperba
Download or read book Computational Graph Theory written by Gottfried Tinhofer and published by Springer. This book was released on 1990-04-20 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: One ofthe most important aspects in research fields where mathematics is "applied is the construction of a formal model of a real system. As for structural relations, graphs have turned out to provide the most appropriate tool for setting up the mathematical model. This is certainly one of the reasons for the rapid expansion in graph theory during the last decades. Furthermore, in recent years it also became clear that the two disciplines of graph theory and computer science have very much in common, and that each one has been capable of assisting significantly in the development of the other. On one hand, graph theorists have found that many of their problems can be solved by the use of com puting techniques, and on the other hand, computer scientists have realized that many of their concepts, with which they have to deal, may be conveniently expressed in the lan guage of graph theory, and that standard results in graph theory are often very relevant to the solution of problems concerning them. As a consequence, a tremendous number of publications has appeared, dealing with graphtheoretical problems from a computational point of view or treating computational problems using graph theoretical concepts.
Download or read book Topics in Algorithmic Graph Theory written by Lowell W. Beineke and published by Cambridge University Press. This book was released on 2021-06-03 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic graph theory has been expanding at an extremely rapid rate since the middle of the twentieth century, in parallel with the growth of computer science and the accompanying utilization of computers, where efficient algorithms have been a prime goal. This book presents material on developments on graph algorithms and related concepts that will be of value to both mathematicians and computer scientists, at a level suitable for graduate students, researchers and instructors. The fifteen expository chapters, written by acknowledged international experts on their subjects, focus on the application of algorithms to solve particular problems. All chapters were carefully edited to enhance readability and standardize the chapter structure as well as the terminology and notation. The editors provide basic background material in graph theory, and a chapter written by the book's Academic Consultant, Martin Charles Golumbic (University of Haifa, Israel), provides background material on algorithms as connected with graph theory.
Download or read book The Practitioner s Guide to Graph Data written by Denise Gosnell and published by O'Reilly Media. This book was released on 2020-03-20 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Download or read book Optimization Problems in Graph Theory written by Boris Goldengorin and published by Springer. This book was released on 2018-09-27 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents open optimization problems in graph theory and networks. Each chapter reflects developments in theory and applications based on Gregory Gutin’s fundamental contributions to advanced methods and techniques in combinatorial optimization. Researchers, students, and engineers in computer science, big data, applied mathematics, operations research, algorithm design, artificial intelligence, software engineering, data analysis, industrial and systems engineering will benefit from the state-of-the-art results presented in modern graph theory and its applications to the design of efficient algorithms for optimization problems. Topics covered in this work include: · Algorithmic aspects of problems with disjoint cycles in graphs · Graphs where maximal cliques and stable sets intersect · The maximum independent set problem with special classes · A general technique for heuristic algorithms for optimization problems · The network design problem with cut constraints · Algorithms for computing the frustration index of a signed graph · A heuristic approach for studying the patrol problem on a graph · Minimum possible sum and product of the proper connection number · Structural and algorithmic results on branchings in digraphs · Improved upper bounds for Korkel--Ghosh benchmark SPLP instances
Download or read book The SAGE Handbook of Social Network Analysis written by John Scott and published by SAGE Publications. This book was released on 2011-05-25 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This sparkling Handbook offers an unrivalled resource for those engaged in the cutting edge field of social network analysis. Systematically, it introduces readers to the key concepts, substantive topics, central methods and prime debates. Among the specific areas covered are: Network theory Interdisciplinary applications Online networks Corporate networks Lobbying networks Deviant networks Measuring devices Key Methodologies Software applications. The result is a peerless resource for teachers and students which offers a critical survey of the origins, basic issues and major debates. The Handbook provides a one-stop guide that will be used by readers for decades to come.
Download or read book Logics in Artificial Intelligence written by Francesco Calimeri and published by Springer. This book was released on 2019-05-06 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th European Conference on Logics in Artificial Intelligence, JELIA 2019, held in Rende, Italy, in May 2019. The 50 full papers and 10 short papers included in this volume were carefully reviewed and selected from 101 submissions. Additionally, the book contains 3 invited papers. The accepted papers span a number of areas within Logics in AI, including: belief revision and argumentation; causal, defeasible and inductive reasoning; conditional, probabilistic and propositional logic; description logics; logic programming; modal and default logic; and temporal logic.
Download or read book Automata Languages and Programming written by Samson Abramsky and published by Springer Science & Business Media. This book was released on 2010-06-30 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 6198 and LNCS 6199 constitutes the refereed proceedings of the 37th International Colloquium on Automata, Languages and Programming, ICALP 2010, held in Bordeaux, France, in July 2010. The 106 revised full papers (60 papers for track A, 30 for track B, and 16 for track C) presented together with 6 invited talks were carefully reviewed and selected from a total of 389 submissions. The papers are grouped in three major tracks on algorithms, complexity and games; on logic, semantics, automata, and theory of programming; as well as on foundations of networked computation: models, algorithms and information management. LNCS 6198 contains 60 contributions of track A selected from 222 submissions as well as 2 invited talks.
Download or read book Algorithms and Computation written by Takao Asano and published by Springer Science & Business Media. This book was released on 2011-11-21 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd International Symposium on Algorithms and Computation, ISAAC 2011, held in Yokohama, Japan in December 2011. The 76 revised full papers presented together with two invited talks were carefully reviewed and selected from 187 submissions for inclusion in the book. This volume contains topics such as approximation algorithms; computational geometry; computational biology; computational complexity; data structures; distributed systems; graph algorithms; graph drawing and information visualization; optimization; online and streaming algorithms; parallel and external memory algorithms; parameterized algorithms; game theory and internet algorithms; randomized algorithms; and string algorithms.
Download or read book Graph Theory with Applications to Algorithms and written by Y. Alavi and published by . This book was released on 1985 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about 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 build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through 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. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and 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 learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, 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 using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.
