融合拓扑势的社交网络层次化社区发现算法

社交网络的社区结构呈现层次性。针对传统凝聚式层次化社区发现算法效率不高以及生成的层次谱图复杂的问题,提出一种融合拓扑势的层次化社区发现算法,利用拓扑势场呈现的自然峰谷结构揭示社交网络社区间的层次关系。该算法搜索局部极大势值节点,并根据局部极大势值节点完成社区的初始划分;根据局部极大势值节点间的距离对初始社区进行迭代合并,直到所有社区被合并为一个社区。在真实社交网络和人工网络上的实验结果表明,该算法能够高效地发现社区的层次结构,生成的层次谱图简单直观。

Hierarchical Community Discovery Algorithm for Social Network on Topology Potential

Social networks often demonstrate hierarchical community structures. Most traditional agglomerative hierarchical community detection methods are often inefficient, and the generated dendrograms are complex. Motivated by solving these problems, the paper proposes a novel hierarchical community detection method on topology potential. The method employs the natural peak-valley structure of topology potential field to reveal the hierarchical relationship among communities in social network. The proposed method firstly identifies the local maximal potential nodes and detects the initial local community structure of the network on the basis of these nodes. After that, the initial local communities are iteratively merged according to the distance among the maximal potential nodes until all the communities are merged into one community. Experimental results on both synthetic and real-world networks show that the proposed method can discover the hierarchical community structure efficiency, and the generated dendrogram is simple and intuitive.