【摘 要】
：
针对经典重叠社团结构发现的派系过滤算法中派系定义过于严格、算法缺乏实用性、时间复杂度高等问题,提出了一种基于共享最近邻的社团结构发现算法.该算法不仅可以对网络进行
【机 构】
：
北京科技大学经济管理学院,北京科技大学中国教育经济信息网管理中心,中国钢研集团,
论文部分内容阅读
针对经典重叠社团结构发现的派系过滤算法中派系定义过于严格、算法缺乏实用性、时间复杂度高等问题,提出了一种基于共享最近邻的社团结构发现算法.该算法不仅可以对网络进行社团结构的划分,而且可以很好地把网络中的桥点找出,算法的时间复杂度约为O(nhk),其中n为网络中的节点数,h为核心社团的数目,k为网络中节点的最大节点度.为了验证该算法的正确率和性能,把该算法应用到计算机生成网络和真实网络中,并与著名的社团探测算法—GN算法和NF快速算法进行了比较.实验的结果表明所提出的算法是有效可行的.
Aiming at the problem of factional definition in the factional filtering algorithm found in the classical overlapping community structure, the definition of factional system is too strict, the algorithm is lack of practicality and time complexity, a new community structure discovery algorithm based on sharing nearest neighbors is proposed. , And can well find out the bridge points in the network. The time complexity of the algorithm is O (nhk), where n is the number of nodes in the network, h is the number of core communities, k is the number of nodes in the network In order to verify the correctness and performance of the algorithm, the algorithm is applied to computer generated networks and real networks, and compared with the well-known community detection algorithm-GN algorithm and NF fast algorithm.Experimental results show The proposed algorithm is effective and feasible.
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