基于信息流动分析的动态社区发现方法

随着社交网络和微博等互联网应用的逐渐流行，其用户规模在迅速膨胀.在这些大规模网络中，社区发现可以为个性化服务推荐和产品推广提供重要依据.不同于传统的网络，这些新型网络的节点之间除了拓扑结构外，还进行频繁的信息交互.信息流动使得这些网络具有方向性和动态性等特征.传统的社区发现方法由于没有考虑到这些新的特征，并不适用于这些新型网络.在传染病动力学理论的基础上，从节点间信息流动的角度，提出一种动态社区发现方法.该方法通过对信息流动的分析来发现联系紧密、兴趣相近的节点集合，以实现动态的社区发现.在真实数据集上的实验结果表明：相对于传统的社区发现方法，所提出的方法能够更准确地发现社区，并且更能体现网络中社区的动态变化.

Dynamic Community Detection Based on Information Flow Analysis

As the Internet applications, such as social networks and micro-blogs, become popular, their scale of users has been increasing rapidly. Community detection in these large-scale networks could provide important insights into customer behavior for service recommendation and product marketing. The difference of these networks from traditional ones is that besides topology, they have frequent information interaction between nodes. Information flow makes these networks directed and dynamic. Traditional community detection approaches fall short in these networks because they do not consider these new characteristics. Inspired by the dynamics of infectious disease theory, this paper proposes a novel community detection approach based on information flow analysis. This approach effectively groups the nodes with frequent information interaction in the same community. Between communities, there would be little information flow. This paper experiments on real-world networks demonstrate that compared with previous community detection methods, the proposed approach is more effective at identifying the dynamics in the networks.