  Graph or network data is ubiquitous in the real world, including social
networks, information networks, traffic networks, biological networks and
various technical networks. The non-Euclidean nature of graph data poses the
challenge for modeling and analyzing graph data. Recently, Graph Neural Network
(GNNs) are proposed as a general and powerful framework to handle tasks on
graph data, e.g., node embedding, link prediction and node classification. As a
representative implementation of GNNs, Graph Attention Networks (GATs) are
successfully applied in a variety of tasks on real datasets. However, GAT is
designed to networks with only positive links and fails to handle signed
networks which contain both positive and negative links. In this paper, we
propose Signed Graph Attention Networks (SiGATs), generalizing GAT to signed
networks. SiGAT incorporates graph motifs into GAT to capture two well-known
theories in signed network research, i.e., balance theory and status theory. In
SiGAT, motifs offer us the flexible structural pattern to aggregate and
propagate messages on the signed network to generate node embeddings. We
evaluate the proposed SiGAT method by applying it to the signed link prediction
task. Experimental results on three real datasets demonstrate that SiGAT
outperforms feature-based method, network embedding method and state-of-the-art
GNN-based methods like signed graph convolutional network (SGCN).
