针对基于模块度最优的社区结构探测算法会产生分辨率限制、时间复杂度高等问题，提出一种基于边密度的社区结构探测算法。该算法不仅可以对网络进行社区结构的划分，而且不会产生分辨率限制的问题，算法的运行复杂度是O（ k· m），其中m为网络中的边数，k为网络中节点的最大节点度。为了验证该算法的正确性和性能，与著名的社团探测算法---GN算法和NF算法进行比较，结果表明所提出的算法是有效可行的。%The community structure detection algorithm based on optimal module degree will have the problems of resolution limit and high time complexity,etc.In light of this,we propose an edge density-based community structure detection algorithm .The algorithm can partition the network in regard to community structure but will not form the problem of resolution limit .The algorithm has the operation complexity of O( k· m) ,where m is the number of edges in the network and k is the maximum degree of node in the network .In order to verify the correctness and performance of the algorithm,we compare it with two of the famous community detection approaches ,namely GN and NF algorithms.Experi-ment results show that the proposed algorithm is feasible and effective .
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