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ABSTRACT 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. KEYWORDS data mining, social networks, power laws, graph generators, pagerank, singular value decomposition.
Christos Faloutsos: To Christina, for her patience, support, and down-toearth questions; to Michalis and Petros, for the ’99 paper that started it all. Deepayan Chakrabarti: To Purna and my parents, for their support and help, and for always being there when I needed them. vii
Page 1: M &C M &C M &C & Morgan Claypool Pu
Page 4 and 5: Synthesis Lectures on Data Mining a
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Page 11 and 12: 1 2 Contents Acknowledgments ......
Page 13 and 14: 10.2.1 The Highly Optimized Toleran
Page 15: Resources .........................
Page 19: PART I Patterns and Laws
Page 22 and 23: 20 3. PATTERNS IN EVOLVING GRAPHS N
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Page 35: IN Tube TENDRILS SCC OUT Disconnect
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Page 42: Authors’ Biographies DEEPAYAN CHA

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