在线社会网络的动态社区发现及其演化

分析了目前动态社区发现及其演化所存在的问题，提出了一种新的动态社区演化方法.该方法利用静态社区挖掘算法提取不同时间快照的每个社区，然后计算出相邻快照的社区之间的演化影响力，进一步分析连续快照中社区结构的发展演化过程.在新浪微博、网络测量Gnutella等大规模实验数据集上的验证，证明了该方法的有效性.此外，实验中还分析了社会网络中节点的出现和消失的频繁程度会影响社区稳定性以及社区结构的演化. 

Detection and Evolution of Dynamic Communities in Online Social Network

It is a critical issue to detect dynamic communities and track their evolution process in online social networks, which can help the controller understand the latent topology, discover anomaly events, predict its evolution trend and control the networks. Firstly, the current flaws of dynamic community detection and its evolution were analyzed. And then a novel approach of dynamic evolution of communities was proposed, including community extract in each time snapshot based on a static community mining algorithm, the calculation of evolution influence between the neighboring snapshots in the community, and generating the evolution process of community structure among continuous snapshots. Finally, tests were carried out based on the large-scale data-sets (e.g. Micro-blog, Gnutella) to validate the approach. The results show the high effectiveness of the approach in community evolution analyzing. In addition, the experiments also analyze the frequency, at which the social network nodes appear and disappear, will affect the community stability and the evolution of the structure.