Abstract
This paper proposes an approach to discover community leaders in a social network by means of a probabilistic time-based graph propagation model. In the study, community leaders are defined as those people who initiate a lot of influence chains according to the number of chains they belong to. To conduct the approach, we define an exponential time-decay function to measure the influence of leaders and construct the chains of leaders’ action-specific influence. Then, we build the general chains by normalizing over all possible users’ actions. In specific, this paper uses the variant of the Apriori algorithm called APPM to mine users’ influence paths. To the best of our knowledge, this work is the first attempt to use the action-specific influence chains for mining community leaders from a social network. In our experiments, two datasets are collected to examine the performance of the proposed algorithm: one is a social network dataset from Facebook consisting of 134 nodes and 517 edges, and another is a citation network dataset from DBLP and Google Scholar containing 2,525 authors, 3,240 papers, and 1,914 citations. In addition, several baselines are also carried out for comparison, including three naive and one user-involved approaches. The experimental results show that, compared with the baselines, for the task of detecting community leaders, the proposed method outperforms the baselines, and our method also obtains good performance on ranking community leaders.
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References
Aggarwal CC, Li Y, Wang J, Wang J (2009) Frequent pattern mining with uncertain data. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, KDD ’09, pp 29–38
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc., San Francisco, VLDB ’94, pp 487–499
Baeza-Yates R, Ribeiro-Neto B, et al (1999) Modern information retrieval, vol 463. ACM press, New York
Bodendorf F, Kaiser C (2009) Detecting opinion leaders and trends in online social networks. In: Proceeding of the 2nd ACM workshop on Social web search and mining, ACM, New York, SWSM ’09, pp 65–68
Burt RS (2009) Structural holes: the social structure of competition. Harvard University Press, Cambridge
Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, KDD ’09, pp 199–208
Esslimani I, Brun A, Boyer A (2010) Detecting leaders in behavioral networks. In: Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, Washington, DC, ASONAM ’10, pp 281–285
Gomez Rodriguez M, Leskovec J, Krause A (2010) Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, KDD ’10, pp 1019–1028
Goyal A, Bonchi F, Lakshmanan LV (2008) Discovering leaders from community actions. In: Proceeding of the 17th ACM conference on Information and knowledge management, ACM, New York, CIKM ’08, pp 499–508
Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining, ACM, New York, WSDM ’10, pp 241–250
Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446. doi:10.1145/582415.582418
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, KDD ’03, pp 137–146
Song X, Chi Y, Hino K, Tseng B (2007) Identifying opinion leaders in the blogosphere. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, ACM, New York, CIKM ’07, pp 971–974
Zhai Z, Xu H, Jia P (2008) Identifying opinion leaders in bbs. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Vol 03, IEEE Computer Society, Washington, DC, USA, pp 398–401. doi:10.1109/WIIAT.2008.37
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Tsai, MF., Tzeng, CW., Lin, ZL. et al. Discovering leaders from social network by action cascade. Soc. Netw. Anal. Min. 4, 165 (2014). https://doi.org/10.1007/s13278-014-0165-9
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DOI: https://doi.org/10.1007/s13278-014-0165-9