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Breaking Bad: Finding Triangle-Breaking Points in Large Networks

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Computational Social Networks (CSoNet 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9197))

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Abstract

Many online social networks (OSN), such as Facebook, Twitter can quickly become popular, but many such as Friendster or MySpace can also suffer catastrophic decline in traffics and users. Understanding the capability of OSNs to withstand perturbation and changes, termed social resilience, is a matter of the uttermost importance. In this paper, we investigate the resilience of OSNs under nodes and links removals, where the robustness of the network is measured through the number of triangles, a fundamental property in many networks. Specifically, we strive to discover critical nodes and links whose failures will critically break most triangles in the network, changing the network’s organization and (possibly) leading to the unpredictable dissolving of the network. We formulate this vulnerability analysis as optimization problems, and provide proofs of their intractability. Given the intractability of the problem, we also investigate approximation algorithms and their efficient implementations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Thang Dinh

  2. Ebay Inc, San Jose, CA, USA

    Ravi Tiwari

Authors
  1. Thang Dinh
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  2. Ravi Tiwari
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Corresponding author

Correspondence to Thang Dinh .

Editor information

Editors and Affiliations

  1. Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA

    My T. Thai

  2. Towsen University, Towson, Maryland, USA

    Nam P. Nguyen

  3. Chinese Academy of Sciences, Beijing, China

    Huawei Shen

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© 2015 Springer International Publishing Switzerland

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Dinh, T., Tiwari, R. (2015). Breaking Bad: Finding Triangle-Breaking Points in Large Networks. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-21786-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21785-7

  • Online ISBN: 978-3-319-21786-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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