Learning Weight Signed Network Embedding with Graph Neural Networks

Abstract

Network embedding aims to map nodes in a network to low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and have achieved state-of-the-art performance in learning node representation. Using fundamental sociological theories (status theory and balance theory) to model signed networks, basing GNN on learning node embedding has become a hot topic in signed network embedding. However, most GNNs fail to use edge weight information in signed networks, and most models cannot be directly used in weighted signed networks. We propose a novel signed directed graph neural networks model named WSNN to learn node embedding for Weighted signed networks. The proposed model reconstructs link signs, link directions, and signed directed triangles simultaneously. Based on the network representations learned by the proposed model, we conduct link sign prediction in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.