SDGNN: Learning Node Representation for Signed Directed Networks

Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i. e. , status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model’s effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embeddings. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods.

Authors

• Junjie Huang Shanghai Jiao Tong University
• Huawei Shen CAS Key Laboratory of AI Safety & Security, Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Liang Hou CAS Key Laboratory of AI Safety & Security, Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Xueqi Cheng CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China

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