Two-stream signed directed graph convolutional network for link prediction

Abstract

Using graph neural networks(GNNs) to transfer and enhance the richness of node information has played an important role in link prediction. However, most traditional GNNs only consider undirected graphs or unsigned graphs, which is limited for information extraction. In order to obtain a richer node representation, we propose a Two-Stream Signed Directed Graph Convolution Network(2S-SDGCN). We consider both the sign and direction when aggregating, which can characterize the topological structure information of the graph. To combine the information together, we conduct a new two-stream network, one for extracting latent factor and transfer pattern features, and the other for obtaining directional positive and negative features. We use five real social networks as datasets to verify the effectiveness of our model. These datasets are usually used as benchmarks to verify the effectiveness of signed network embedding. Experiments show that our model outperforms the existing methods.