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Top-K structural diversity search in large networks

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Abstract

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 study two types of structural diversity measures, namely, component-based structural diversity measure and core-based structural diversity measure. For component-based structural diversity, 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 search strategies are proposed. We also design efficient techniques to handle frequent updates in dynamic networks and maintain the top-\(k\) results. We further show how the techniques proposed in component-based structural diversity measure can be extended to handle the core-based structural diversity measure. Extensive experimental studies are conducted in real-world large networks and synthetic graphs, and the results demonstrate the efficiency and effectiveness of the proposed methods.

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Notes

  1. https://www.kddcup2012.org.

  2. http://dblp.uni-trier.de/xml/.

  3. http://www.netcom-analyzer.org/datasets/166.

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Acknowledgments

This work is supported by the Hong Kong Research Grants Council (RGC) General Research Fund (GRF) Project Nos. CUHK 411211, 418512, 14209314, the Chinese University of Hong Kong Direct Grant Nos. 4055015, 4055048, NSFC Grants No. 61402292, and Natural Science Foundation of SZU Grant No. 201438. Lu Qin is supported by ARC DE140100999.

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Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, New Territories, Hong Kong

    Xin Huang, Hong Cheng & Jeffrey Xu Yu

  2. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen, China

    Rong-Hua Li

  3. Quantum Computation and Intelligent Systems, Department of Engineering and Information Technology, University of Technology, Sydney, Australia

    Lu Qin

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  1. Xin Huang
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  2. Hong Cheng
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  3. Rong-Hua Li
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  4. Lu Qin
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  5. Jeffrey Xu Yu
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Correspondence to Hong Cheng.

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Huang, X., Cheng, H., Li, RH. et al. Top-K structural diversity search in large networks. The VLDB Journal 24, 319–343 (2015). https://doi.org/10.1007/s00778-015-0379-0

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