A network representation method based on edge information extraction

Abstract

In recent years, network representation learning has attracted extensive attention in the academic field due to its significant application potential. However, most of the methods cannot explore edge information in the network deeply, resulting in poor performance at downstream tasks such as classification, clustering and link prediction. In order to solve this problem, we propose a novel way to extract network information. First, the original network is transformed into an edge network with structure and edge information. Then, edge representation vectors can be obtained directly by using an existing network representation model with edge network as its input. Node representation vectors can also be obtained by utilizing the relationships between edges and nodes. Compared with the structure of original network, the edge network is denser, which can help solving the problems caused by sparseness. Extensive experiments on several real-world networks demonstrate that edge network outperforms original network in various graph mining tasks, i.e., node classification and node clustering.