Abstract
Recently, user influence in social networks has been studied extensively. Many applications related to social influence depend on quantifying influence and finding the most influential users of a social network. Most existing work studies the global influence of users, i.e. the aggregated influence that a user has on the entire network. It is often overlooked that users may be significantly more influential to some audience groups than others. In this paper, we propose AudClus, a method to detect audience groups and identify group-specific influencers simultaneously. With extensive experiments on real data, we show that AudClus is effective in both the task of detecting audience groups and the task of identifying influencers of audience groups. We further show that AudClus makes possible for insightful observations on the relation between audience groups and influencers. The proposed method leads to various applications in areas such as viral marketing, expert finding, and data visualization.
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References
Bakshy, E., Hofman, J.M., Watts, D.J., Mason, W.A.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM (2011)
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: WSDM (2013)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM (2010)
Chen, W., Wang, Y.: Efficient influence maximization in social networks. In: KDD (2009)
Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: ICDM (2010)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6 Pt 2), 066111 (2004)
Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal, Complex Systems 1695 (2006)
Eftekhar, M., Ganjali, Y., Koudas, N.: Information cascade at group scale. In: KDD (2013)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM (2010)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)
Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: KDD (2009)
Lin, S., Hu, Q., Wang, G., Yu, P.S.: Understanding community effects on information diffusion. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015, Part I. LNCS (LNAI), vol. 9077, pp. 82–95. Springer, Heidelberg (2015)
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM (2010)
Mehmood, Y., Barbieri, N., Bonchi, F., Ukkonen, A.: CSI: community-level social influence analysis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part II. LNCS, vol. 8189, pp. 48–63. Springer, Heidelberg (2013)
Newman, M.E.J., Leicht, E.A.: Mixture models and exploratory analysis in networks. PNAS 104(23), 9564–9569 (2007)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD (2009)
Tang, J., Zhang, J., Yao, L., Li, J., Zhong, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: KDD (2008)
Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: KDD (2010)
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Lin, S., Hu, Q., Zhang, J., Yu, P.S. (2015). Discovering Audience Groups and Group-Specific Influencers. In: Appice, A., Rodrigues, P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9285. Springer, Cham. https://doi.org/10.1007/978-3-319-23525-7_34
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DOI: https://doi.org/10.1007/978-3-319-23525-7_34
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