Detecting or recognizing event related change points in dynamic networks becomes an increasingly important task, as a change in network's structure may associate with a change in function of the networked system. However, general change point detection methods either fail to extract effective features or do not scale well. In this work, we introduce the probability distribution of nodes’ importance to characterize a network, the profile that allows for comparison between two networks and clustering on snapshots of dynamic networks. Based on this, we develop summarization scheme to detect change points on dynamical networks by segmenting the snapshots into disjoint clusters, which can guarantee the scalability on large dynamical networks. Specifically, we construct a network whose nodes represent the dynamic network snapshots. Then we do community detection on the constructed network and serialize the community detection results in chronological order. The resultant sequence naturally indicates the potential changes. Experiments on both synthetic and real-world networks show the outperformance of our framework compared to the state-of-the-art methods.

中文翻译：

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基于社区识别的动态网络变化点检测

检测或识别动态网络中与事件相关的变化点变得越来越重要，因为网络结构的变化可能与网络系统功能的变化相关联。然而，一般的变化点检测方法要么无法提取有效特征，要么不能很好地扩展。在这项工作中，我们引入了节点重要性的概率分布来表征网络，该配置文件允许在两个网络之间进行比较并在动态网络的快照上进行聚类。在此基础上，我们开发了通过将快照分割成不相交的集群来检测动态网络上变化点的总结方案，这可以保证大型动态网络的可扩展性。具体来说，我们构建了一个网络，其节点代表动态网络快照。然后我们对构建的网络进行社区检测，并按时间顺序序列化社区检测结果。由此产生的序列自然地表明了潜在的变化。在合成网络和现实世界网络上的实验表明，与最先进的方法相比，我们的框架具有更好的性能。