Finding Spread Blockers in Dynamic Networks

  • Habiba
  • Yintao Yu
  • Tanya Y. Berger-Wolf
  • Jared Saia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5498)


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.


Dynamic Network Betweenness Centrality Spreading Process Dynamic Cluster Aggregate Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Habiba
    • 1
  • Yintao Yu
    • 2
  • Tanya Y. Berger-Wolf
    • 1
  • Jared Saia
    • 3
  1. 1.University of Illinois at Chicago 
  2. 2.University of Illinois at Urbana-Champaign 
  3. 3.University of New Mexico 

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