Online Sampling of High Centrality Individuals in Social Networks

  • Arun S. Maiya
  • Tanya Y. Berger-Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6118)


In this work, we investigate the use of online or “crawling” algorithms to sample large social networks in order to determine the most influential or important individuals within the network (by varying definitions of network centrality). We describe a novel sampling technique based on concepts from expander graphs. We empirically evaluate this method in addition to other online sampling strategies on several real-world social networks. We find that, by sampling nodes to maximize the expansion of the sample, we are able to approximate the set of most influential individuals across multiple measures of centrality.


Social Network Centrality Measure Original Network Closeness Centrality Jaccard Similarity 
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

  • Arun S. Maiya
    • 1
  • Tanya Y. Berger-Wolf
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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