Local node feature modeling for edge computing based on network embedding in dynamic networks

Abstract

In a dynamic network, the characteristics of local nodes include first and higher-order proximity among the nodes as well as different attributes attached to each node. This complexity impose significant challenge for dynamic network modeling. As a result, few dynamic network studies have considered high-order proximity among local nodes. In this paper, we adopt the network embedding method to map high-order proximity of local nodes into low-dimensional, dense and real-valued vectors. Morevoer, we incorporate it into a model-based evolutionary clustering method through regularity conditions. Such a unified framework can increase the effectiveness and robustness of dynamic community detection while pertaining a good explanatory and visualization ability. Experiments based on synthetic and real world data sets show that our model can produce better community detection results than other popular models such as DECS and Genlouvain in dense networks. This result is consistent with the advantage of network embedding method in dense networks.