FVBM: A Filter-Verification-Based Method for Finding Top-k Closeness Centrality on Dynamic Social Networks
Closeness centrality is often used to identify the top-k most prominent nodes in a network. Real networks, however, are rapidly evolving all the time, which results in the previous methods hard to adapt. A more scalable method that can immediately react to the dynamic network is demanding. In this paper, we endeavour to propose a filter and verification framework to handle such new trends in the large-scale network. We adopt several pruning methods to generate a much smaller candidate set so that bring down the number of necessary time-consuming calculations. Then we do verification on the subset; which is a much time efficient manner. To further speed up the filter procedure, we incremental update the influenced part of the data structure. Extensive experiments using real networks demonstrate its high scalability and efficiency.
KeywordsCloseness centrality Filter-Verification Dynamic social network
This work was supported by Natural Science Foundation of China (No. 61170003).
- 2.Olsen, P.W., Labouseur, A.G., Hwang, J.H.: Efficient top-k closeness centrality search. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 196–207 (2014)Google Scholar
- 3.Sariyuce, A.E., Kaya, K., Saule, E., Catalyurek, U.V.: Incremental algorithms for network management and analysis based on closeness centrality, arXiv preprint (2013). arXiv:1303.0422
- 4.Yen, C.C., Yeh, M.Y., Chen, M.S.: An efficient approach to updating closeness centrality and average path length in dynamic networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 867–876. IEEE (2013)Google Scholar