为了挖掘动态网络的社团结构并跟踪其演变模式,首先,提出社团演变算法FEDN.提出一个基于结构相似度的静态社团挖掘算法CDA,将动态网络建模成不同时刻的网络序列,对任意时刻的网络应用CDA得到不同时刻的过程社团集合；然后,根据社团演变事件的特征,形式化社团演变事件模型,计算过程社团与前一时刻时间序列社团集合的相似度,得到其演变方式；最终得到能够真正反映社团结构的稳定的社团集合以及社团演变的多条轨迹.通过在真实数据集以及合成数据集上进行实验,验证了算法的可行性及有效性.%A community evolution algorithm FEDN is proposed to find the community structures and track the evolutionary patterns of dynamic networks. First of all, a structure similarity based algorithm CDA is presented to mine the community structures of static networks. The evolving networks are formalized into static snapshots at different time stamps and then CDA is used to obtain the interim communities. Secondly, FEDN calculates the similarities between interim communities and previous time sequence community sets and then gets the evolving mode according to the evolutionary patterns which is established on the basis of the characteristics of community evolutionary events. Ultimately, FEDN gains the stable community sets and multiple evolving traces of the communities. The experimental results on the real and synthetic datasets show that FEDN is practical and effective.
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