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Graph mining and influence propagation

Published: 30 October 2008 Publication History

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.

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  1. Graph mining and influence propagation

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    cover image ACM Conferences
    WICOW '08: Proceedings of the 2nd ACM workshop on Information credibility on the web
    October 2008
    100 pages
    ISBN:9781605582597
    DOI:10.1145/1458527
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 30 October 2008

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    1. graph mining

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    • Keynote

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    CIKM08
    CIKM08: Conference on Information and Knowledge Management
    October 30, 2008
    California, Napa Valley, USA

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    Overall Acceptance Rate 9 of 19 submissions, 47%

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