Top-K Structural Diversity Search in Large Network

Publisher:
ACM
Publication Type:
Conference Proceeding
Citation:
Proceedings of the VLDB Endowment, 2013, 6 pp. 1618 - 1629
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013005190OK.pdf709.9 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: