SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship

Abstract

Many complex social systems can be modeled as directed signed networks whose edges are marked with a positive/negative sign or direction. Network embedding representation is aimed at mapping rich structural and semantic information into low-dimensional vectors, and extensive research has demonstrated that Graph Neural Networks (GNNs) are an effective way. However, existing GNNs are primarily designed for undirected signed networks and usually used to capture the semantics of the complex structure by social structural balance theory, thus omitting the directional information of the links. In this research, we introduce a reciprocity relationship and status theory to enhance the modeling of the directed positive/negative relationship between two nodes, which has been widely applied in complex network research, and design SR-GNN, a GNN model for signed directed networks, to enable a more accurate vector representation of the nodes and convolution operations on edges with different directions and signs. Experiments demonstrate a reciprocity relationship, and status theory can allow the model to extract the most essential comprehensive information in signed directed graphs. Furthermore, SR-GNN can obtain effective status scores of nodes for link sign predictions and node ranking tasks, both of which yield state-of-the-art performance in most cases.

Keywords

• signed directed network
• graph representation learning
• graph convolution
• reciprocal information
• status theory