MERP: Motifs enhanced network embedding based on edge reweighting preprocessing

Abstract

Network embedding has attracted a lot of attention in different fields recently. It represents nodes in a network into a low-dimensional and dense space while preserving the structural properties of the network. Some methods (e.g. motif2Vec, RUM, and MODEL) have been proposed to preserve the higher-order structures, i.e., motifs in embedding space, and they have obtained better results in some downstream network analysis tasks. However, there still exists a significant challenge because original motifs may include redundant noise edges, and embedding entire motifs into embedding space may adversely affect the performance in downstream tasks. To overcome this problem, we propose a motifs enhancement framework for network embedding, based on edge reweighting. Through edge reweighting, the weight of redundant noise edges between motifs is decreased. Therefore, the effect of redundant noise edges will be reduced in the embedding space. We apply the edge reweighting as a preprocessing phase in network embedding, and construct the motifs enhanced network by incorporating enhanced motifs structures with the original network. By doing this, the embedding vectors from the motifs enhanced network can achieve better performance in downstream network analysis tasks. Extensive experiments are performed on two network analysis tasks (community detection and node classification) with synthetic and real-world datasets. The results show that our framework outperforms state-of-the-art network embedding methods.