Graph mining and influence propagation

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Graph mining and influence propagation

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Source

Conference on Information and Knowledge Management archive
Proceeding of the 2nd ACM workshop on Information credibility on the web table of contents

Napa Valley, California, USA

SESSION: Keynote addresses table of contents

Pages 1-2

Year of Publication: 2008

ISBN:978-1-60558-259-7

Author

Christos Faloutsos

Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 8, Downloads (12 Months): 52, Citation Count: 0

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ABSTRACT

How do graphs look like? How do they evolve over time? How can we generate realistic-looking graphs? We review some static and temporal 'laws', and we describe the ``Kronecker'' graph generator, which naturally matches all of the known properties of real graphs. We also describe some case studies.

The first is on influence and virus propagation on real graphs, where we show that the so-called ``epidemic threshold'' of a graph depends only on the first eigenvalue of the adjacency matrix. The second shows how to spot patterns in e-bay interaction graphs, indicative of the ``non-delivery'' type of fraud. The last is analysis on blog cascades and some surprising patterns there.

INDEX TERMS

Primary Classification:
H. Information Systems
H.2 DATABASE MANAGEMENT
H.2.8 Database applications
Subjects: Data mining