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Finding Spread Blockers in Dynamic Networks

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Advances in Social Network Mining and Analysis (SNAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5498))

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Abstract

Abstract. Social interactions are conduits for various processes spreading through a population, from rumors and opinions to behaviors and diseases. In the context of the spread of a disease or undesirable behavior, it is important to identify blockers: individuals that are most effective in stopping or slowing down the spread of a process through the population. This problem has so far resisted systematic algorithmic solutions. In an effort to formulate practical solutions, in this paper we ask: Are there structural network measures that are indicative of the best blockers in dynamic social networks? Our contribution is two-fold. First, we extend standard structural network measures to dynamic networks. Second, we compare the blocking ability of individuals in the order of ranking by the new dynamic measures. We found that overall, simple ranking according to a node’s static degree, or the dynamic version of a node’s degree, performed consistently well. Surprisingly the dynamic clustering coefficient seems to be a good indicator, while its static version performs worse than the random ranking. This provides simple practical and locally computable algorithms for identifying key blockers in a network.

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Author information

Authors and Affiliations

  1. University of Illinois at Chicago,  

    Habiba & Tanya Y. Berger-Wolf

  2. University of Illinois at Urbana-Champaign,  

    Yintao Yu

  3. University of New Mexico,  

    Jared Saia

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  1. Habiba
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  2. Yintao Yu
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  3. Tanya Y. Berger-Wolf
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  4. Jared Saia
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Editor information

Editors and Affiliations

  1. College of Information Science and Technology, Pennsylvania State University, 16802, University Park, PA, USA

    Lee Giles

  2. Microsoft Research, One Microsoft Way, Redmond, Washington, USA

    Marc Smith

  3. College of Information Sciences and Technology, The Pennsylvania State University, 16802, Unversity Park, PA, USA

    John Yen

  4. Amazon.com., Seattle, WA, USA

    Haizheng Zhang

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Habiba, Yu, Y., Berger-Wolf, T.Y., Saia, J. (2010). Finding Spread Blockers in Dynamic Networks. In: Giles, L., Smith, M., Yen, J., Zhang, H. (eds) Advances in Social Network Mining and Analysis. SNAKDD 2008. Lecture Notes in Computer Science, vol 5498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14929-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-14929-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-14929-0

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