Top-K structural diversity search in large networks

Xin Huang*, Hong Cheng, Rong Hua Li, Lu Qin, Jeffrey Xu Yu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

24 Citations (Scopus)


Social contagion depicts a process of information (e.g., fads, opinions, news) diffusion in the online social networks. A recent study reports that in a social contagion process the probability of contagion is tightly controlled by the number of connected components in an individual's neighborhood. Such a number is termed structural diversity of an individual and it is shown to be a key predictor in the social contagion process. Based on this, a fundamental issue in a social network is to find top-k users with the highest structural diversities. In this paper, we, for the first time, study the top-k structural diversity search problem in a large network. Specifically, we develop an effective upper bound of structural diversity for pruning the search space. The upper bound can be incrementally refined in the search process. Based on such upper bound, we propose an efficient framework for top-k structural diversity search. To further speed up the structural diversity evaluation in the search process, several carefully devised heuristic search strategies are proposed. Extensive experimental studies are conducted in 13 real-world large networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

Original languageEnglish
Pages (from-to)1618-1629
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number13
Publication statusPublished - Aug 2013
Event39th International Conference on Very Large Data Bases, VLDB 2012 - Trento, Italy
Duration: 26 Aug 201330 Aug 2013

Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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