Download or read book Algorithms and Models for Network Data and Link Analysis written by François Fouss and published by Cambridge University Press. This book was released on 2016-07-12 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.
Download or read book Link Mining Models Algorithms and Applications written by Philip S. Yu and published by Springer Science & Business Media. This book was released on 2010-09-16 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.
Download or read book Algorithms and Models for the Web Graph written by Bogumił Kamiński and published by Springer Nature. This book was released on 2020-06-02 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 17th International Workshop on Algorithms and Models for the Web Graph, WAW 2020, held in Warsaw, Poland, in September 2020. The 12 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The aim of the workshop was to further the understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs. Due to the corona pandemic the conference was postponed from June 2020 to September 2020.
Download or read book Social Network Data Analytics written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2011-03-18 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.
Download or read book Data Structures and Network Algorithms written by Robert Endre Tarjan and published by SIAM. This book was released on 1983-01-01 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms.
Download or read book Network Algorithms Data Mining and Applications written by Ilya Bychkov and published by Springer. This book was released on 2020-02-23 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.
Download or read book Complex Networks Their Applications VI written by Chantal Cherifi and published by Springer. This book was released on 2017-11-24 with total page 1290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the VI International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2017), which took place in Lyon on November 29 – December 1, 2017. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and ecological networks and technological networks.
Download or read book Complex Networks and Their Applications VIII written by Hocine Cherifi and published by Springer Nature. This book was released on 2019-11-26 with total page 1047 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.
Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Network Analysis written by Ulrik Brandes and published by Springer. This book was released on 2005-02-02 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: ‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
Download or read book Advances in Computational Algorithms and Data Analysis written by Sio-Iong Ao and published by Springer Science & Business Media. This book was released on 2008-09-28 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Computational Algorithms and Data Analysis offers state of the art tremendous advances in computational algorithms and data analysis. The selected articles are representative in these subjects sitting on the top-end-high technologies. The volume serves as an excellent reference work for researchers and graduate students working on computational algorithms and data analysis.
Download or read book A User s Guide to Network Analysis in R written by Douglas Luke and published by Springer. This book was released on 2015-12-14 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.
Download or read book Defense Modeling Simulation and Analysis written by National Research Council and published by National Academies Press. This book was released on 2006-10-22 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling, simulation, and analysis (MS&A) is a crucial tool for military affairs. MS&A is one of the announced pillars of a strategy for transforming the U.S. military. Yet changes in the enterprise of MS&A have not kept pace with the new demands arising from rapid changes in DOD processes and missions or with the rapid changes in the technology available to meet those demands. To help address those concerns, DOD asked the NRC to identify shortcomings in current practice of MS&A and suggest where and how they should be resolved. This report provides an assessment of the changing mission of DOD and environment in which it must operate, an identification of high-level opportunities for MS&A research to address the expanded mission, approaches for improving the interface between MS&A practitioners and decision makers, a discussion of training and continuing education of MS&A practitioners, and an examination of the need for coordinated military science research to support MS&A.
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Download or read book Graph Powered Machine Learning written by Alessandro Negro and published by Simon and Schuster. This book was released on 2021-10-05 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrade 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
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
