Abstract：Community structure detection is extensively studied. However, the performance of the existing community detection methods becomes lower as the noise in the related networks increases. To solve this problem, the prior knowledge in the form of pairwise constraints and existing community detection methods are combined to guide the process of community detection, and an extremal optimization based semi-supervised algorithm is proposed for community detection. The experimental results on networks show that compared with the existing methods, the proposed method improves the accuracy of community detection and shows good performance with the noise in the network.
杜梅，胡学钢，李磊，何伟. 基于网络结构极值优化的半监督社团检测方法*[J]. 模式识别与人工智能, 2015, 28(2): 162-172.
DU Mei, HU Xue-Gang, LI Lei, HE Wei. Network Structure-Enhanced Extremal Optimization Based Semi-supervised Algorithm for Community Detection. , 2015, 28(2): 162-172.
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