Abstract
User-centered analysis is one of the aims of online community search. In this paper, we study personalized top-n influential community search that has a practical application. Given an evolving social network, where every edge has a propagation probability, we propose a maximal pk-Clique community model, that uses a new cohesive criterion. The criterion requires that the propagation probability of each edge or each maximal influence path between two vertices that is considered as an edge, is greater than p. The maximal clique problem is an NP-hard problem, and the introduction of this cohesive criterion makes things worse, as it may add new edges to existing networks. To conduct personalized top-n influential community search efficiently in such networks, we first introduce a search space refinement method. We then present pruning based and heuristic based search approaches. The proposed algorithms more than double the efficiency of the search performance for basic solutions. The effectiveness and efficiency of our algorithms have been verified using four real datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bron, C., Kerbosch, J.: Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16(9), 575–577 (1973)
Cui, W., Xiao, Y., Wang, H., Lu, Y., Wang, W.: Online search of overlapping communities. In: Proceedings of the ACM SIGMOD, pp. 277–288 (2013)
Danon, L., Dazguilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech. Theory Exp. 2005(09) (2005)
Eppstein, D., Maarten, L., Strash, D.: Listing all maximal cliques in sparse graphs in near-optimal time. Comput. Sci. 6506, 403–414 (2010)
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)
Granovetter, M.: Threshold models of collective behavior. 83, 1420–1443 (1978)
Huang, X., Cheng, H., Qin, L., Tian, W., Yu, J.X.: Querying k-truss community in large and dynamic graphs. In: Proceedings of the ACM SIGMOD, pp. 1311–1322 (2014)
Huang, X., Lakshmanan, L.V.S., Xu, J.: Community search over big graphs: models, algorithms, and opportunities. In: IEEE International Conference on Data Engineering, pp. 1451–1454 (2017)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the ACM SIGKDD, pp. 137–146 (2003)
Lee, J.R., Chung, C.W.: A query approach for influence maximization on specific users in social networks. IEEE Trans. Knowl. Data Eng. 27(2), 340–353 (2015)
Li, H.P., Hu, H., Xu, J.: Nearby friend alert: location anonymity in mobile geosocial networks. IEEE Pervasive Comput. 12(4), 62–70 (2013)
Li, J., Wang, X., Deng, K., Yang, X., Sellis, T., Yu, J.X.: Most influential community search over large social networks. In: Proceedings of the ICDE, pp. 871–882 (2017)
Li, R.H., Qin, L., Yu, J.X., Mao, R.: Finding influential communities in massive networks. VLDB J. 2, 1–26 (2017)
Li, R.H., Yu, J.X., Mao, R.: Efficient core maintenance in large dynamic graphs. IEEE Trans. Knowl. Data Eng. 26(10), 2453–2465 (2014)
Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Ruan, J., Zhang, W.: An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: Seventh IEEE International Conference on Data Mining, pp. 643–648 (2007)
Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques and computational experiments. Theor. Comput. Sci. 363(1), 28–42 (2006)
Wang, J., Cheng, J., Fu, W.C.: Redundancy-aware maximal cliques. In: Proceedings of the ACM SIGKDD, pp. 122–130 (2013)
Wang, M., Wang, C., Yu, J.X., Zhang, J.: Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proc. VLDB Endow. 8(10), 998–1009 (2015)
Zhu, Q., Hu, H., Xu, C., Xu, J., Lee, W.C.: Geo-social group queries with minimum acquaintance constraints. VLDB J. 26(5), 1–19 (2014)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61572165), the Natural Science Foundation of Zhejiang Province (No. LZ15F 020003). Xiaoyi Fu’s work is supported by Hong Kong Research Grants Council (No. 12200817, 12201615 and 12258116).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Xu, J., Fu, X., Tu, L., Luo, M., Xu, M., Zheng, N. (2018). Personalized Top-n Influential Community Search over Large Social Networks. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-96890-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96889-6
Online ISBN: 978-3-319-96890-2
eBook Packages: Computer ScienceComputer Science (R0)