Résumé: Network embedding has facilitated lots of network analytical tasks by representing nodes as low-dimensional vectors. As an extension of convolutional neural networks from Euclidean data to irregular data, Graph Convolutional Networks (GCNs) provide a novel way to learn network representations and have attracted widespread attention currently. Most of the existing GCNs are only applied to unsigned networks. However, networks could have both positive and negative links in the real world, to which the unsigned algorithms are no longer applicable. In this paper, we propose a novel Signed Directed Attention Network model to capture the structural and social theoretical information of signed directed networks comprehensively through an auto-encoder framework. In the encoding block, the information of sign, direction, social theory, and "bridge" edges are encoded into node embeddings by a node fine-grained classification aggregation layer. Besides, a direction parameterization layer is also introduced to convert directions into direction-specific convolutional filters to enhance the node embeddings. In the decoding block, loss functions are designed to model sign, direction, "bridge" edge, and social theory information accordingly and make them complementary to each other to capture the network information fully. Experimental results for the signed link prediction task on several real-world signed directed graphs show that the proposed framework can achieve state-of-the-art performance.

Langue: Anglais