基于局部信息检测的多粒度社团挖掘方法

Method of Multi-granularity Community Structure Mining Based on Local Information Detection

从复杂性和动态性特征出发,给出了复杂网络局部模块度的定义,并提出了基于局部信息检测的社团发现算法,认为局部模块度值最大的节点集合就是最理想的社团结构。在此基础上提出了多粒度社团挖掘方法,为多视图观察复杂网络结构特征提供了新的研究思路。最后的实验分析表明了方法的有效性和可行性。

Taking consideration of complexity and dynamic of complex networks, a definition of local modularity was proposed, and an algorithm for communication structure mining based on local information detection was given, with the criterion, i, e. the best community is the node group whose local modularity is the largest. Then a method of multi-granularity community structure mining was proposed, which provides new ideas to observe structure characters of complex networks from various angles. Final experiments verify its efficiency and feasibility.