SDEGNN: Signed graph neural network for link sign prediction enhanced by signed distance encoding.

The existing signed graph neural networks mainly focus on the design process of neighbor aggregation function, but ignore the correlation between nodes, which leads to the decline of the representation ability of neural networks. In order to solve the above problems, a SDEGNN (Signed Distance Encoding based on Graph Neural Network) model based on enhanced signed distance encoding is proposed in this paper. Firstly, the problem of limited representation ability in signed graph neural networks is discussed. Secondly, in order to capture the correlation between nodes, signed distance encoding is proposed as the node feature representation to enhance the representation ability of the model. Thirdly, the signed distance encoding is injected into the information aggregation process of the signed graph convolutional network, and the objective function is proposed to optimize the SDEGNN model. The SDEGNN model is verified performance by three real signed network datasets Bitcoin-OTC, Bitcoin-Alpha, and Wiki-RfA. The experimental results show that the SDEGNN model can effectively improve the accuracy of link sign prediction tasks. [ABSTRACT FROM AUTHOR]