  Given a signed social graph, how can we learn appropriate node
representations to infer the signs of missing edges? Signed social graphs have
received considerable attention to model trust relationships. Learning node
representations is crucial to effectively analyze graph data, and various
techniques such as network embedding and graph convolutional network (GCN) have
been proposed for learning signed graphs. However, traditional network
embedding methods are not end-to-end for a specific task such as link sign
prediction, and GCN-based methods suffer from a performance degradation problem
when their depth increases. In this paper, we propose Signed Graph Diffusion
Network (SGDNet), a novel graph neural network that achieves end-to-end node
representation learning for link sign prediction in signed social graphs. We
propose a random walk technique specially designed for signed graphs so that
SGDNet effectively diffuses hidden node features. Through extensive
experiments, we demonstrate that SGDNet outperforms state-of-the-art models in
terms of link sign prediction accuracy.
