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The Research of Community Mining Based on Granular Computing and Quasi Complete Subgraph

机译:基于粒计算和拟完全子图的社区挖掘研究

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With the idea of granular computing, the node similarity measure based on network topology is improved, which is used to measure the similarity between nodes and communities, also between communities. Based on the similarity measure, a concept of Quasi Complete Subgraph is given and an algorithm for detecting community structure is proposed, namely community structure mining algorithm based on ? - quasi complete sub graph. Finally, in comparing with GN algorithm and CNM algorithm, the proposed algorithm is tested on three real social networks in various scales. The results indicate that the algorithm proposed is effective. And it is indicated that the community structure detected by this algorithm will not be influenced by different initial nodes.
机译:借助粒度计算的思想,改进了基于网络拓扑的节点相似性度量,该度量用于度量节点与社区之间以及社区之间的相似性。基于相似度度量,给出了拟完全子图的概念,提出了一种检测社团结构的算法,即基于α的社团结构挖掘算法。 -准完整子图。最后,与GN算法和CNM算法进行比较,在三种不同规模的真实社交网络上对该算法进行了测试。结果表明该算法是有效的。并且表明该算法检测到的群落结构不会受到不同初始节点的影响。

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