Research on signed directed network link prediction based on dual attention mechanism

To address the over-reliance on sociological and structural balance theories in analysing directed signed networks, which inadequately describe the true relationships between nodes, this paper proposes the DADSGNN model. DADSGNN is a decoupled signed graph neural network that employs a dual attention mechanism to enhance the quality of node embedding representations. Firstly, DADSGNN decouples node features into multiple potential factors within its encoder, employing the concept of decoupled representation learning. This allows for a deeper exploration of potential factors influencing internodal relationships. Secondly, a dual attention mechanism, comprising local and structural attention, is applied. This enables the model to classify and aggregate diverse types of neighbour information and subsequently calculate the characteristics of each potential influence factor. Such an approach effectively improves the accuracy and interpretability of the model's representation of complex internodal relationships. Thirdly, the decoder component of DADSGNN incorporates a novel decoder for the link sign prediction task. This decoder fully considers the correlation between different potential factors, thereby significantly improving prediction accuracy. Finally, the predictive capability and validity of DADSGNN are evaluated using directed network datasets of varying scales, thereby verifying the model's performance on real-world signed network data.