一种基于局部扩展优化的重叠社区发现算法

挖掘复杂网络的重叠社区结构对研究复杂系统具有重要的理论和实践意义。提出一种基于局部扩展优化的重叠社区识别算法。 首先基于网络节点的聚集系数筛选种子节点，选取不相关的、局部聚集系数大的种子作为初始社区；然后采用贪心策略扩展初始社区，得到局部连接紧密的自然社区；最后检测并合并相似的社区，获得高覆盖率的重叠社区结构。在人工生成网络和真实网络数据集上的实验结果表明，与现有的基于局部扩展的代表性重叠社区发现算法相比，所提算法能在稀疏程度不同的网络上发现更高质量的重叠社区。

An overlapping community detection algorithm based on local expansion optimization

Overlapping community structure detection bears both theoretical and practical significance for the study of complex systems. We propose an overlapping community detection algorithm based on local expansion optimization. Firstly, a group of irrelevant seeds with large clustering coefficient are selected as initial communities according to the clustering coefficient of network nodes. Then, the initial communities are expanded into tightly connected local communities by a greedy strategy. Finally, similar communities are merged and overlapping community structures with high cover rate are obtained. Experimental results on both synthetic and real world networks show that compared with other representative local expansion methods, the proposed algorithm can efficiently detect overlapping communities of higher quality in the networks with different sparsity degrees.