Graph mining : laws, tools, and case studies
著者
書誌事項
Graph mining : laws, tools, and case studies
（Synthesis lectures on data mining and knowledge discovery, #6）
Morgan & Claypool Publishers, c2012
大学図書館所蔵 全4件
注記
Includes bibliographical references (p. 167-190)
内容説明・目次
内容説明
What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others.
In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints.
目次
Introduction
Patterns in Static Graphs
Patterns in Evolving Graphs
Patterns in Weighted Graphs
Discussion: The Structure of Specific Graphs
Discussion: Power Laws and Deviations
Summary of Patterns
Graph Generators
Preferential Attachment and Variants
Incorporating Geographical Information
The RMat
Graph Generation by Kronecker Multiplication
Summary and Practitioner's Guide
SVD, Random Walks, and Tensors
Tensors
Community Detection
Influence/Virus Propagation and Immunization
Case Studies
Social Networks
Other Related Work
Conclusions
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