1.
2. Department of Economic Management, Wuhan University of Technology Huaxia College, Wuhan Hubei 430223, China
3. School of Economics, Wuhan University of Technology, Wuhan Hubei 430070, China
Abstract：To cope with the low accuracy of the mining results in the existing community discovery algorithms and the low quality of intelligent recommendation in the Web services resource, on the basis of the conventional collaborative filtering algorithms, a dynamic community discovery algorithm was proposed based on the nodes' similarity. Firstly, the central node that had the most connected nodes was regarded as the initial network community, and the community contribution degree was taken as the metric to continuously form a plurality of global saturated contribution degree communities. Then, an overlapping calculation was used to merge the communities of high similarity. Finally, the calculated results were arranged in descending order to form neighboring user sets for obtaining community recommendation object by calculating the dynamic similarity between target user and other users in the community. The experimental results show that the user social network community classification by the proposed community discovery algorithms is consistent with the real community classification results. The proposed algorithm can improve the accuracy of the community mining and helps to achieve high-quality community recommendation.
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2013, Vol. 33 
