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中文摘要：
用最优化算法逼近网络特征矩阵以获取网络的降维描述是网络团模糊聚类的一个重要途径;在最优化算法设计上,多余约束会过滤掉有意义的拓扑信息;以提高模糊聚类精度为目的,以引入新的点团关系度量为基础,建立了一个约束更少的最优目标函数,并用一种对称式矩阵分解算法实施逼近;新度量中保留了更多网络拓扑信息,所得聚类结果较传统的模糊隶属度更为精确,在两种计算机模拟网络上的实验证明了该方法能提高网络聚类精度,在两个真实网络上的实验也获得了很好的效果。
英文摘要：
Using the optimization method to approximate network feature matrix is an important approach for conventional fuzzy community detection.However,unnecessary constraints would filter out some meaningful topology information.In this paper,based on introducing a novel clique-node relationship metric,this paper constructed a new objective function with less constraint and solved it by the method of symmetrical nonnegative matrix factorization.The new metric retains more topology information of network and shows high accuracy in uncovering the real partition of the network,especially for the overlapping community detection.The computational results of the method on artificial and real networks confirm its ability.
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