A Dynamic Network Change Detection Method Using Network Embedding

Dynamic networks are ubiquitous. Detecting dynamic network changes is helpful to understand the network development trend and discover network anomalies in time. It is a research hotspot at present. The structure of the network in the real world is very complex, the current feature learning method is difficult to capture a variety of network connectivity patterns, and the definition of efficient network features requires a large number of neighborhood knowledge and computational costs. In order to overcome this limitation, this paper presents a method of dynamic network change detection using network embedding, which automates the whole process by using feature extraction as a embedding problem, and carries out dynamic network change detection by analyzing the distribution of nodes in space after network embedding processing. We use this method to simulate dynamic network and real dynamic network datasets to prove the validity of this method.