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An Improved Local Community Detection Algorithm Using Selection Probability
Author(s) -
Shixiong Xia,
Ranran Zhou,
Yong Zhou,
Mu Zhu
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/406485
Subject(s) - node (physics) , selection (genetic algorithm) , local optimum , computer science , algorithm , local search (optimization) , state (computer science) , selection algorithm , local community , value (mathematics) , mathematical optimization , data mining , artificial intelligence , mathematics , machine learning , engineering , ecology , structural engineering , biology
In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods

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