摘要：

提出一种基于粗糙集的社区结构发现算法。将信息中心度作为衡量节点之间关联度的标准，在处理社区间边界节点时引入粗糙集中的上下近似集概念。将网络中的各个节点划分到社区中，从而将复杂网络划分成k个社区，k值由算法自动选定，并通过模块度确定理想的社区结构。在Zachary Karate Club模型和College Football Network模型上进行验证，实验结果表明，该算法的准确率较高。

关键词: 社区结构, 节点关联度, 粗糙集, 上近似集, 下近似集

Abstract:

This paper proposes a new detection algorithm based on rough set. It uses information centrality as a measure of correlation between nodes. While dealing with the boundary nodes between communities, it uses upper and lower approximations subsets so as to better simulate the real world, then it clusters nodes to certain community and identify the network to k communities, identifies the ideally community structure according to modularity, besides the k value need not to be prior given. The algorithm is tested on two network dataset named Zachary Karate Club and College Football. and experimental result shows it has high accuracy rate.

Key words: community structure, node relevance degree, rough set, upper approximations, lower approximations

中图分类号: 

• TP391.4