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Team formation with influence maximization for influential event organization on social networks

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Abstract

Online event-based social services allow users to organize social events by specifying the themes, and invite friends to participate social events. While the event information can be spread over the social network, it is expected that by certain communication between event hosts, users interested in the event themes can be as more as possible. In this paper, by combining the ideas of team formation and influence maximization, we formulate a novel research problem, Influential Team Formation (ITF), to facilitate the organization of social events. Given a set L of required labels to describe the event topics, a social network, and the size k of the host team, ITF is to find a k-node set S that satisfying L and maximizing the Influence-Cost Ratio (i.e., the influence spread per communication cost between team members). Since ITF is proved to be NP-hard, we develop two greedy algorithms and one heuristic method to solve it. Extensive experiments conducted on Facebook and Google+ datasets exhibit the effectiveness and efficiency of the proposed methods. In addition, by employing the real event participation data in Meetup, we show that ITF with the proposed solutions is able to predict organizers of influential events.

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Notes

  1. www.meetup.com

  2. www.plancast.com

References

  1. Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of ACM International Conference on World Wide Web, WWW ’12, pp 839–848 (2012)

  2. Basiri, J., Taghiyareh, F., Ghorbani, A.: Collaborative team formation using brain drain optimization: a practical and effective solution. World Wide Web Journal (2017)

  3. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp 199–208 (2009)

  4. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pp 1029–1038 (2010)

  5. Cheng, S., Shen, H., Huang, J., Zhang, G., Cheng, X.: Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of ACM International Conference on Information & Knowledge Management, CIKM ’13, pp 509–518 (2013)

  6. Cheng, S., Shen, H., Huang, J., Chen, W., Cheng, X.: Imrank: influence maximization via finding self-consistent ranking. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, SIGIR ’14, pp 475–484 (2014)

  7. Kargar, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM ’11, pp 985–994 (2011)

  8. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, pp 137–146 (2003)

  9. Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp 467–476 (2009)

  10. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07, pp 420–429 (2007)

  11. Li, C.-T., Shan, M.-K.: Composing activity groups in social networks. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM ’12, pp 2375–2378 (2012)

  12. Li, C.-T., Shan, M.-K., Lin, S.-D.: On team formation with expertise query in collaborative social networks. Knowl. Inf. Syst. 42(2), 441–463 (2015)

    Article  Google Scholar 

  13. Li, K., Lu, W., Bhagat, S., Lakshmanan, L.V., Yu, C.: On social event organization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pp 1206–1215 (2014)

  14. Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y.-R., Buchler, N.: Replacing the irreplaceable: fast algorithms for team member recommendation. In: Proceedings of ACM International Conference on World Wide Web, WWW ’15, pp 636–646 (2015)

  15. Liu, Q., Xiang, B., Chen, E., Xiong, H., Tang, F., Yu, J.X.: Influence maximization over large-scale social networks: a bounded linear approach. In: Proceedings of ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, pp 171–180 (2014)

  16. Liu, X., Li, S., Liao, X., Peng, S., Wang, L., Kong, Z.: Know by a handful the whole sack: efficient sampling for top-k influential user identification in large graphs. World Wide Web Journal 17(4), 627–647 (2014)

    Article  Google Scholar 

  17. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximation for maximizing submodular set functions. Math. Program. 14(1), 265–294 (1978)

    Article  MathSciNet  Google Scholar 

  18. Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E. 64, 1–7 (2001)

  19. Ohsaka, N., Akiba, T., Yoshida, Y., Kawarabayashi, K.-I.: Fast and accurate influence maximization on large networks with pruned Monte-Carlo simulations. In: Proceedings of AAAI Conference on Artificial Intelligence, AAAI ’14, pp 138–144 (2014)

  20. Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. In: Proceedings of ACM International Conference on World Wide Web, WWW ’13, pp 1077–1088 (2013)

  21. Shen, C.-Y., Yang, D.-N., Lee, W.-C., Chen, M.-S.: Maximizing friend-making likelihood for social activity organization. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD ’15, pp 3–15 (2015)

  22. Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pp 939–948 (2010)

  23. Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web Journal 19(6), 1151–1177 (2016)

    Article  Google Scholar 

  24. Xiang, B., Liu, Q., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: Pagerank with priors: an influence propagation perspective. In: Proceedings of International Joint Conference on Artificial Intelligence, IJCAI ’13, pp 2740–2746 (2013)

  25. Yang, D.-N., Shen, C.-Y., Lee, W.-C., Chen, M.-S.: On socio-spatial group query for location-based social networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, pp 949–957 (2012)

  26. Yu, Z., Zhang, D., Yu, Z., Yang, D.: Participant selection for offline event marketing leveraging location-based social networks. IEEE Trans. Syst. Man Cybern. Syst. 45(6), 853–864 (2015)

    Article  Google Scholar 

  27. Zhang, E., Wang, G., Gao, K., Yu, G.: Finding critical blocks of information diffusion in social networks. World Wide Web Journal 18(3), 731–747 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. National Cheng Kung University, No. 1, University Road, Tainan City, 701, Taiwan, Republic of China

    Cheng-Te Li

  2. Technische Universität München, ArcisstraBe 21, D-80333, Munich, Germany

    Mei-Yuan Huang

  3. Peking University, No. 5, Yiheyuan Road., Haidian, Beijing, China

    Rui Yan

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  1. Cheng-Te Li
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  2. Mei-Yuan Huang
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  3. Rui Yan
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Correspondence to Cheng-Te Li.

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Li, CT., Huang, MY. & Yan, R. Team formation with influence maximization for influential event organization on social networks. World Wide Web 21, 939–959 (2018). https://doi.org/10.1007/s11280-017-0492-7

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