一种联合拓扑与属性的社区模糊划分算法

现有的社区发现算法通常基于结构特性进行社区划分，对节点属性特征欠缺考虑。为此，提出一种基于模糊等价关系的社区发现算法。用完全相异距离指数的概念将拓扑结构与属性特征相结合，以此作为隶属关系建立模糊等价关系矩阵，选择合适的聚类f阂值对网络进行社区划分。实验结果证明，与传统的GN算法相比，该算法发现社区的准确率较高，在相同社区内的节点连接紧密且具有同质性。

Community Fuzzy Dividing Algorithm Combining Topology and Attribute

Aiming at the problem that most existing community detecting algorithms are usually based on the structure characteristics of network and lack of consideration attribute infottnation, a community detection algorithm is proposed based on fuzzy equivalence relation combining topology and attribute in social networks. In this algorithm, a new concept of integrated dissimilarity distance index is used for combining topology and attribute, and it is regarded as the subordinate relation to build the fuzzy equivalence relation matrix, appropriate clustering threshold value is choses for community detection. Experimental result proves that the algorithm has high accuracy compared with those traditional GN algorithms, and nodes in the same community are densely connected as well as homogeneous.