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A Study of Community Detection and Dynamic Properties in Social Networks

Author: ShenZuoZuo
Tutor: YangXiaoKang；SongLi
School: Shanghai Jiaotong University
Course: Signal and Information Processing
Keywords: social network diffusion community structure personalized reorder
CLC: O157.5
Type: Master's thesis
Year: 2011
Downloads: 533
Quote: 1
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Abstract


All kinds of social networks play a more and more important role in ourdaily lives. By analyzing users’ behaviors in the social networks, we can gainthe insight of the way a message propagating in the social network and get toknow how to promote new ideas and prevent spams and rumors fromepidemic spreading.The study of the social network currently is troubled by the problem thatthe amount of the social network data is tremendous and the most valuableinformation is concealed in the whole dataset. In this work we tackle thisproblem from two aspects.For the community detection section: we have proposed a hierarchicaldiffusion method to detect the community structure from very large socialnetworks. By using the network of communities instead of the network ofpeople, we can reduce the dimension of the social network greatly. Ourcommunity detection algorithm is based on the local structure, so it’s veryefficient. Tests on both classical and synthetic benchmarks show that ouralgorithm is comparable to state of the art community detection algorithms inboth computational complexity and accuracy.For the dynamic properties section: we infer users’ interests from theirbehaviors in social networks. Then we present a supervised learning methodfor personalized tweets reordering based on users’ interests. Twitter displaysthe tweets a user received in a reversed chronological order, which is notalways the best choice because many informative or relevant tweets might beflooded or displayed at the bottom due to some nonsense buzzes. Throughexploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant andnon relevant tweets. The tweets are then reordered according to the predictedrelevance scores from the learned model. Experimental results with realtwitter user activities demonstrated the effectiveness of our method. The newmethod achieved above 30% accuracy gain compared with the defaultordering in twitter based on time.

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