效率演算法於適地性社群網路之影響力點搜尋

This thesis investigates finding the influential nodes in the location-based social network (LBSN). When the users write comments about a location, it is likely that their writing behavior is influenced by others. We observe the real LBSN data and propose a general method to find the influential nodes in an LBSN. We use the Foursquare data to fit the information diffusion model and compute each node’s influence degree, and use a greedy algorithm to find the set of top-k influential nodes of the LBSN. The set of top-k influential nodes can influence the largest number of nodes in an LBSN. The previously best way to find influential nodes uses the information diffusion model to trace all of the nodes in an LBSN and thus costs a lot of time. This thesis uses the information diffusion model to trace each user’s friends and proposes an algorithm to extract the k nodes with high influence among friends. Compared with the previously best result, we show that our method saves a lot of time and the error of our approximation is small.

並列關鍵字

Data mining ； Graph mining ； Social Network

參考文獻

[1] N. Benchettara, R. Kanawati and C. Rouveirol, “Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks,” ASONAM, 2010, pp. 326–330.

[2] X. Cai, M. Bain, A. Krzywicki, W. Wobcke, Y. S. Kim, P. Compton and A. Mahidadia, “Reciprocal and Heterogeneous Link Prediction in Social Networks,” PAKDD, 2012, pp. 193–204.

[3] E. Cho, S. A. Myers and J. Leskovec, “Friendship and Mobility: User Movement in Location-Based Social Network,” KDD, 2011, pp. 1082–1090.

[4] W. Chen, Y. Wang and S. Yang, “Efficient Influence Maximization in Social Network,” KDD, 2009, pp. 199–208.

[5] S. Debnath, N. Ganguly, P. Mitra, “Feature Weighting in Content Based Recommendation System Using Social Network Analysis,” WWW, 2008, pp. 1041–1042.

國際替代計量

效率演算法於適地性社群網路之影響力點搜尋

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