| Description |
1 online resource |
| Series |
Computer communications and networks, 1617-7975 |
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Computer communications and networks.
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| Contents |
Intro; Preface; Network; Graph; Organization of the Book; Network Datasets; Contents; List of Figures; List of Tables; 1 Basics of Graph Theory; 1.1 Choice of Representation; 1.1.1 Undirected Graphs; 1.1.2 Directed Graphs; 1.2 Degree of a Vertex; 1.3 Degree Distribution; 1.4 Average Degree of a Graph; 1.5 Complete Graph; 1.6 Regular Graph; 1.7 Bipartite Graph; 1.8 Graph Representation; 1.8.1 Adjacency Matrix; 1.8.2 Edge List; 1.8.3 Adjacency List; 1.9 Edge Attributes; 1.9.1 Unweighted Graph; 1.9.2 Weighted Graph; 1.9.3 Self-looped Graph; 1.9.4 Multigraphs; 1.10 Path; 1.11 Cycle |
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1.12 Path Length1.13 Distance; 1.14 Average Path Length; 1.15 Diameter; 1.16 Connectedness of Graphs; 1.17 Clustering Coefficient; 1.18 Average Clustering Coefficient; Reference; 2 Graph Structure of the Web; 2.1 Algorithms; 2.1.1 Breadth First Search (BFS) Algorithm; 2.1.2 Strongly Connected Components (SCC) Algorithm; 2.1.3 Weakly Connected Components (WCC) Algorithm; 2.2 First Set of Experiments-Degree Distributions; 2.3 Second Set of Experiments-Connected Components; 2.4 Third Set of Experiments-Number of Breadth First Searches; 2.5 Rank Exponent mathcalR; 2.6 Out-Degree Exponent mathcalO |
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2.7 Hop Plot Exponent mathcalH2.8 Eigen Exponent mathcalE; References; 3 Random Graph Models; 3.1 Random Graphs; 3.2 Erdös-Rényi Random Graph Model; 3.2.1 Properties; 3.2.2 Drawbacks of Gn, p; 3.2.3 Advantages of Gn, p; 3.3 Bollobás Configuration Model; 3.4 Permutation Model; 3.5 Random Graphs with Prescribed Degree Sequences; 3.5.1 Switching Algorithm; 3.5.2 Matching Algorithm; 3.5.3 ̀̀Go with the Winners'' Algorithm; 3.5.4 Comparison; References; 4 Small World Phenomena; 4.1 Small World Experiment; 4.2 Columbia Small World Study; 4.3 Small World in Instant Messaging; 4.4 Erdös Number |
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4.5 Bacon Number4.6 Decentralized Search; 4.7 Searchable; 4.8 Other Small World Studies; 4.9 Case Studies; 4.9.1 HP Labs Email Network; 4.9.2 LiveJournal Network; 4.9.3 Human Wayfinding; 4.10 Small World Models; 4.10.1 Watts-Strogatz Model; 4.10.2 Kleinberg Model; 4.10.3 Destination Sampling Model; References; 5 Graph Structure of Facebook; 5.1 HyperANF Algorithm; 5.2 Iterative Fringe Upper Bound (iFUB) Algorithm; 5.3 Spid; 5.4 Degree Distribution; 5.5 Path Length; 5.6 Component Size; 5.7 Clustering Coefficient and Degeneracy; 5.8 Friends-of-Friends; 5.9 Degree Assortativity |
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5.10 Login Correlation5.11 Other Mixing Patterns; 5.11.1 Age; 5.11.2 Gender; 5.11.3 Country of Origin; References; 6 Peer-To-Peer Networks; 6.1 Chord; 6.2 Freenet; Problems; References; 7 Signed Networks; 7.1 Theory of Structural Balance; 7.2 Theory of Status; 7.3 Conflict Between the Theory of Balance and Status; 7.4 Trust in a Network; 7.4.1 Atomic Propagations; 7.4.2 Propagation of Distrust; 7.4.3 Iterative Propagation; 7.4.4 Rounding; 7.5 Perception of User Evaluation; 7.6 Effect of Status and Similarity on User Evaluation; 7.6.1 Effect of Similarity on Evaluation |
| Summary |
This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed August 29, 2018) |
| Subject |
Online social networks.
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Python (Computer program language)
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Information retrieval.
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Network hardware.
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COMPUTERS -- General.
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Online social networks
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Python (Computer program language)
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| Form |
Electronic book
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| Author |
Mohan, Ankith, author
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Srinivasa, K. G., author.
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| ISBN |
9783319967462 |
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3319967460 |
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