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題名 Discovering Leaders from Social Network by Action Cascade
作者 Tsai, Ming-Feng ; Tzeng, Chih-Wei ; Lin, Zhe-Li ; Chen, Arbee L. P.
蔡銘峰
貢獻者 資科系
關鍵詞 Social Network Analysis; Path Mining; Opinion Leader Discovery
日期 2014
上傳時間 21-Mar-2014 17:46:46 (UTC+8)
摘要 This paper proposes an approach to discovering community leaders in social network by means of a probabilistic time-based graph propagation model. To conduct the approach, we define an exponential decay function for influence as a function of time, and build action-specific influence chains by multiplying path propagation values; then, we create general influence chains by normalizing over all possible actions. In the study, our approach identifies community leaders as those people whose initiated influence chains are relatively more than the chains they involved. In our experiments, a small Facebook network dataset with 134 nodes and 517 edges is employed to assess the performance of the proposed method. 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, the proposed method can effectively identify community leaders within the social network, achieving 0.8 in terms of F-measure.
關聯 Social Network Analysis and Mining,Proceedings of the Fifth Workshop on Social Network Systems
資料類型 article
DOI http://dx.doi.org/10.1145/2181176.2181188
dc.contributor 資科系en_US
dc.creator (作者) Tsai, Ming-Feng ; Tzeng, Chih-Wei ; Lin, Zhe-Li ; Chen, Arbee L. P.en_US
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2014en_US
dc.date.accessioned 21-Mar-2014 17:46:46 (UTC+8)-
dc.date.available 21-Mar-2014 17:46:46 (UTC+8)-
dc.date.issued (上傳時間) 21-Mar-2014 17:46:46 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64820-
dc.description.abstract (摘要) This paper proposes an approach to discovering community leaders in social network by means of a probabilistic time-based graph propagation model. To conduct the approach, we define an exponential decay function for influence as a function of time, and build action-specific influence chains by multiplying path propagation values; then, we create general influence chains by normalizing over all possible actions. In the study, our approach identifies community leaders as those people whose initiated influence chains are relatively more than the chains they involved. In our experiments, a small Facebook network dataset with 134 nodes and 517 edges is employed to assess the performance of the proposed method. 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, the proposed method can effectively identify community leaders within the social network, achieving 0.8 in terms of F-measure.en_US
dc.format.extent 615036 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Social Network Analysis and Mining,Proceedings of the Fifth Workshop on Social Network Systemsen_US
dc.subject (關鍵詞) Social Network Analysis; Path Mining; Opinion Leader Discovery-
dc.title (題名) Discovering Leaders from Social Network by Action Cascadeen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1145/2181176.2181188en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1145/2181176.2181188en_US