Network Embedding and Change Modeling in Dynamic Heterogeneous Networks

Abstract

Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.