Abstract
Most existing studies in community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. However, the bulk of literature ignores the interaction strength among the users in the retrieved communities. As a result, many members of the detected communities do not interact frequently to each other. This inactivity will create problem for online advertisers as they require the community to be highly interactive to efficiently diffuse marketing information. In this paper, we study the problem of detecting attribute-driven active intimate community, that is, for a given input query consisting a set of attributes, we want to find densely-connected communities in which community members actively participate as well as have strong interaction (intimacy) with respect to the given query attributes. We design a novel attribute relevance intimacy score function for the detected communities and establish its desirable properties. To this end, we use an indexed based solution to efficiently discover active intimate communities. Extensive experiments on real data sets show the effectiveness and performance of our proposed method.
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A tradition in which the users can recommend their followers to follow more people.
References
Weng, W., Zhu, S., Xu, H.: Hierarchical community detection algorithm based on local similarity. J. Digit. Inf. Manag. 12(4), 274–280 (2014)
Sun, X., Wang, H., Li, J., Zhang, Y.: Injecting purpose and trust into data anonymisation. Comput. Secur. 30(5), 332–345 (2011)
Correa, D., Sureka, A., Pundir, M.: iTop - interaction based topic centric community discovery on Twitter. In: PIKM, pp. 51–58 (2012)
Chen, J., Saad, Y.: Dense subgraph extraction with application to community detection. TKDE 24(7), 1216–1230 (2012)
Li, J., Sellis, T., Culpepper, J.S., He, Z., Liu, C., Wang, J.: Geo-social influence spanning maximization. TKDE 29(8), 1653–1666 (2017)
Dev, H., Ali, M.E., Hashem, T.: User interaction based community detection in online social networks. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8422, pp. 296–310. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05813-9_20
Zhang, J., Tao, X., Wang, H.: Outlier detection from large distributed databases. WWW 17(4), 539–568 (2014)
Lim, K.H., Datta, A.: An interaction-based approach to detecting highly interactive Twitter communities using tweeting links. In: Web Intelligence, pp. 1–15 (2016)
Fang, Y., Cheng, R., Luo, S., Hu, J.: Effective community search for large attributed graphs. VLDB 9, 1233–1244 (2016)
Li, J., Liu, C., Yu, J.X., Chen, Y., Sellis, T., Culpepper, J.S.: Personalized influential topic search via social network summarization. TKDE 28(7), 1820–1834 (2016)
Bogdanov, P., Busch, M., Moehli, J., Singh, A.K., Szymanski, B.K.: The social media genome: modeling individual topic-specific behavior in social media. In: ASONAM, pp. 236–242 (2013)
Sun, X., Wang, H., Li, J., Zhang, Y.: Satisfying privacy requirements before data anonymization. Comput. J. 55(4), 422–437 (2012)
Singh, S., Awekar, A.: Incremental shared nearest neighbor density-based clustering. In: CIKM, pp. 1533–1536 (2013)
Li, J., Wang, X., Deng, K., Yang, X., Sellis, T., Yu, J.X.: Most influential community search over large social networks. In: ICDE, pp. 871–882 (2017)
Acknowledgment
This work is supported by the ARC Discovery Projects DP160102412 and DP170104747.
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Anwar, M.M., Liu, C., Li, J. (2018). Uncovering Attribute-Driven Active Intimate Communities. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_9
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DOI: https://doi.org/10.1007/978-3-319-92013-9_9
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