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Analyze sampling methods to large-scale community detection based on measurements of the quality for the community structure of sampled and original graph
The Hong Kong University of Science and Technology Department of Computer Science and Engineering Title: "Analyze sampling methods to large-scale community detection based on measurements of the quality for the community structure of sampled and original graph" by Mr. Dai Jie Abstract: As the social network size has expanded greatly, the size of network may have millions of nodes. Present clustering algorithms focus more on a relatively smaller size, and not applicable on large community detection; hence, we would like to sample the graph and then infer community structure from the sample graph. This project mainly discusses the sampling qualities of the present sampling algorithms: Random Node Sampling, Forest Fire Sampling and Snowball Expansion Sampler. After evaluating these three algorithms, we found that Random Node Sampling had the best behavior, Forest Fire Sampling ranked the second and Snowball Expansion Sampler behaved the worst. Date : 11 May 2012 (Friday) Time : 3pm to 3:40pm Venue : 5487 (lift 25-26) Advisor : Prof. D.Y. Yeung 2nd reader : Prof. Nevin Zhang