模块度增量与局部模块度引导下的社区发现算法

社区结构是复杂网络的重要特性之一，基于层次聚类的社区发现算法很好地利用了模块度来挖掘网络中的社区结构，但其局限性也导致算法对社区结构复杂的网络划分不够准确、无法发现小于一定规模的社区。在层次聚类的基础上，提出引入局部模块度来弥补模块度在划分社区时的不足，避免可能出现的划分不合理情况。通过真实数据集和人工网络进行了验证，实验结果证明，该算法具有可行性与有效性。

Communities detection during increment of modularity and local modularity

Community structure is one of the important characteristics of Complex Network, the community detection algorithms with hierarchical clustering makes good use of modularity to achieve community detection, but its limitations lead to poor detection quality of networks which have complex structure of community and it may fail to identify communities smaller than a scale. This paper used local modularity to make up the lack of modularity on the basis of hierarchical clustering, it can avoid unreasonable result. The algorithm is tested on real-world data sets and artificial network, experimental results confirm that it has feasibility and effectiveness.