Download or read book Efficient Graph Representations written by Jeremy P. Spinrad and published by American Mathematical Soc.. This book was released on 2003-01-01 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Efficient Graph Representations written by Jeremy P. Spinrad and published by American Mathematical Soc.. This book was released on 2003-01-01 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book deals with questions which arise from storing a graph in a computer. Different classes of graphs admit different forms of computer representations, and focusing on the representations gives a new perspective on a number of problems. For a variety of classes of graphs, the book considers such questions as existence of good representations, algorithms for finding representations, questions of characterizations in terms of representation, and how the representation affects the complexity of optimization problems. General models of efficient computer representations are also considered. The book is designed to be used both as a text for a graduate course on topics related to graph representation, and as a monograph for anyone interested in research in the field of graph representation. The material is of interest both to those focusing purely on graph theory and to those working in the area of graph algorithms.

Download or read book The Boost Graph Library written by Jeremy G. Siek and published by Pearson Education. This book was released on 2001-12-20 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Boost Graph Library (BGL) is the first C++ library to apply the principles of generic programming to the construction of the advanced data structures and algorithms used in graph computations. Problems in such diverse areas as Internet packet routing, molecular biology, scientific computing, and telephone network design can be solved by using graph theory. This book presents an in-depth description of the BGL and provides working examples designed to illustrate the application of BGL to these real-world problems. Written by the BGL developers, The Boost Graph Library: User Guide and Reference Manual gives you all the information you need to take advantage of this powerful new library. Part I is a complete user guide that begins by introducing graph concepts, terminology, and generic graph algorithms. This guide also takes the reader on a tour through the major features of the BGL; all motivated with example problems. Part II is a comprehensive reference manual that provides complete documentation of all BGL concepts, algorithms, and classes. Readers will find coverage of: Graph terminology and concepts Generic programming techniques in C++ Shortest-path algorithms for Internet routing Network planning problems using the minimum-spanning tree algorithms BGL algorithms with implicitly defined graphs BGL Interfaces to other graph libraries BGL concepts and algorithms BGL classes–graph, auxiliary, and adaptor Groundbreaking in its scope, this book offers the key to unlocking the power of the BGL for the C++ programmer looking to extend the reach of generic programming beyond the Standard Template Library.

Download or read book Graph Algorithms in the Language of Linear Algebra written by Jeremy Kepner and published by SIAM. This book was released on 2011-01-01 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current exponential growth in graph data has forced a shift to parallel computing for executing graph algorithms. Implementing parallel graph algorithms and achieving good parallel performance have proven difficult. This book addresses these challenges by exploiting the well-known duality between a canonical representation of graphs as abstract collections of vertices and edges and a sparse adjacency matrix representation. This linear algebraic approach is widely accessible to scientists and engineers who may not be formally trained in computer science. The authors show how to leverage existing parallel matrix computation techniques and the large amount of software infrastructure that exists for these computations to implement efficient and scalable parallel graph algorithms. The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.

Download or read book 2020 IEEE International Conference on Image Processing ICIP written by IEEE Staff and published by . This book was released on 2020-10-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing ICIP 2020, the 27th in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world

Download or read book Mastering Algorithms with Perl written by Jarkko Hietaniemi and published by "O'Reilly Media, Inc.". This book was released on 1999-08-18 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many programmers would love to use Perl for projects that involve heavy lifting, but miss the many traditional algorithms that textbooks teach for other languages. Computer scientists have identified many techniques that a wide range of programs need, such as: Fuzzy pattern matching for text (identify misspellings!) Finding correlations in data Game-playing algorithms Predicting phenomena such as Web traffic Polynomial and spline fitting Using algorithms explained in this book, you too can carry out traditional programming tasks in a high-powered, efficient, easy-to-maintain manner with Perl.This book assumes a basic understanding of Perl syntax and functions, but not necessarily any background in computer science. The authors explain in a readable fashion the reasons for using various classic programming techniques, the kind of applications that use them, and -- most important -- how to code these algorithms in Perl.If you are an amateur programmer, this book will fill you in on the essential algorithms you need to solve problems like an expert. If you have already learned algorithms in other languages, you will be surprised at how much different (and often easier) it is to implement them in Perl. And yes, the book even has the obligatory fractal display program.There have been dozens of books on programming algorithms, some of them excellent, but never before has there been one that uses Perl.The authors include the editor of The Perl Journal and master librarian of CPAN; all are contributors to CPAN and have archived much of the code in this book there."This book was so exciting I lost sleep reading it." Tom Christiansen

Download or read book Graph Based Representations in Pattern Recognition written by Luc Brun and published by Springer Science & Business Media. This book was released on 2005-03-23 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th IAPR International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2005, held in Poitiers, France in April 2005. The 18 revised full papers and 17 revised poster papers presented were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on graph representations, graphs and linear representations, combinatorial maps, matching, hierarchical graph abstraction and matching, inexact

Download or read book Semi Supervised Learning written by Olivier Chapelle and published by MIT Press. This book was released on 2010-01-22 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

Download or read book Graph Based Representations in Pattern Recognition written by Xiaoyi Jiang and published by Springer Science & Business Media. This book was released on 2011-05-10 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2011, held in Münster, Germany, in May 2011. The 34 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on graph-based representation and characterization, graph matching, classification, and querying, graph-based learning, graph-based segmentation, and applications.

Download or read book Network Embedding written by Cheng Yang and published by Morgan & Claypool Publishers. This book was released on 2021-03-25 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Download or read book Data Structures with Go written by Aditya Pratap Bhuyan and published by Aditya Pratap Bhuyan. This book was released on 2024-08-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Data Structures with Go: A Comprehensive Guide," your gateway to mastering data structures using the Go programming language. In today’s fast-paced software development world, a solid grasp of data structures is essential for creating efficient, scalable, and high-performance applications. This book provides a thorough exploration of data structures through Go, a language known for its simplicity, performance, and robust concurrency support. Why This Book? Data structures are fundamental to computer science and software engineering. They determine how data is organized, stored, and manipulated, significantly impacting the performance and efficiency of algorithms. With Go’s growing popularity for its clean syntax and effective concurrency model, it is an excellent choice for learning and implementing data structures. This book leverages Go’s features to offer practical insights into data structures, making it a valuable resource for developers of all skill levels. What You Will Learn Fundamentals of Data Structures: The book starts with an introduction to data structures, highlighting their importance and role in software development. You’ll explore basic data types in Go and their applications in various data structures. Arrays and Slices: Delve into arrays and slices, foundational structures in Go. Learn how to declare, initialize, and manipulate them, and understand their performance implications and practical uses. Linked Lists: Explore singly and doubly linked lists, including their structures, operations, and Go implementations. Understand how linked lists compare to arrays and slices and their advantages and limitations. Stacks and Queues: Study these essential linear data structures. Learn about stack (LIFO) and queue (FIFO) operations and their implementations in Go. The chapter also covers variants like deques and priority queues. Trees: Understand hierarchical data structures such as binary trees, binary search trees (BST), AVL trees, and Red-Black trees. Learn about tree operations, traversal techniques, and their Go implementations. Graphs: Learn about graph representations, including adjacency matrices and adjacency lists, and explore directed and undirected graphs. This chapter also covers common algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS). Hashing: Discover hashing techniques, hash tables, and collision handling strategies. Implement hash tables in Go and understand their practical applications. Advanced Data Structures: Dive into specialized data structures such as heaps, tries, suffix trees, and Bloom filters. Learn about their implementations and use cases. Algorithms and Data Structures in Practice: Apply data structures to real-world problems. This chapter focuses on sorting and searching algorithms, optimization techniques, and performance profiling in Go. Real-World Applications: Explore how data structures are used in practical projects. Study case studies, best practices, and design patterns for implementing data structures in Go-based systems. Who Should Read This Book? This book caters to: Beginners: Those new to Go or data structures will find a clear, structured introduction. Intermediate Developers: Readers with some experience can deepen their knowledge and tackle advanced topics. Experienced Professionals: Those looking to explore Go or stay updated with modern practices will find valuable insights and practical examples. Learning Approach Emphasizing hands-on learning, the book includes practical examples, exercises, and real-world case studies to reinforce understanding and encourage experimentation. By working through these exercises, you will gain practical experience and a deeper grasp of data structures in Go. "Data Structures with Go: A Comprehensive Guide" is your key to mastering essential computer science principles and applying them effectively in modern applications. Dive in and discover how Go can enhance your skills in building robust, efficient, and scalable systems. Aditya

Download or read book Graph based Knowledge Representation written by Michel Chein and published by Springer Science & Business Media. This book was released on 2008-10-20 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a de?nition and study of a knowledge representation and r- soning formalism stemming from conceptual graphs, while focusing on the com- tational properties of this formalism. Knowledge can be symbolically represented in many ways. The knowledge representation and reasoning formalism presented here is a graph formalism – knowledge is represented by labeled graphs, in the graph theory sense, and r- soning mechanisms are based on graph operations, with graph homomorphism at the core. This formalism can thus be considered as related to semantic networks. Since their conception, semantic networks have faded out several times, but have always returned to the limelight. They faded mainly due to a lack of formal semantics and the limited reasoning tools proposed. They have, however, always rebounded - cause labeled graphs, schemas and drawings provide an intuitive and easily und- standable support to represent knowledge. This formalism has the visual qualities of any graphic model, and it is logically founded. This is a key feature because logics has been the foundation for knowledge representation and reasoning for millennia. The authors also focus substantially on computational facets of the presented formalism as they are interested in knowledge representation and reasoning formalisms upon which knowledge-based systems can be built to solve real problems. Since object structures are graphs, naturally graph homomorphism is the key underlying notion and, from a computational viewpoint, this moors calculus to combinatorics and to computer science domains in which the algorithmicqualitiesofgraphshavelongbeenstudied,asindatabasesandconstraint networks.

Download or read book Graph based Natural Language Processing and Information Retrieval written by Rada Mihalcea and published by Cambridge University Press. This book was released on 2011-04-11 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Download or read book Graph Based Representation and Reasoning written by Nathalie Hernandez and published by Springer. This book was released on 2014-07-17 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st International Conference on Conceptual Structures, ICCS 2014, held in Iaşi, Romania, in July 2014. The 17 regular papers and 6 short papers presented in this volume were carefully reviewed and selected from 40 and 10 submissions, respectively. The topics covered are: conceptual structures, knowledge representation, reasoning, conceptual graphs, formal concept analysis, semantic Web, information integration, machine learning, data mining and information retrieval.

Download or read book Graph Based Representations in Pattern Recognition written by Andrea Torsello and published by Springer Science & Business Media. This book was released on 2009-07-09 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2009, held in Venice, Italy in May 2009. The 37 revised full papers presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on graph-based representation and recognition, graph matching, graph clustering and classification, pyramids, combinatorial maps, and homologies, as well as graph-based segmentation.