DGLP: Incorporating Orientation Information for Enhanced Link Prediction in Directed Graphs

Link prediction in directed graphs offers a solution for uncovering detailed and accurate relationships among distinct entities. Unlike conventional link prediction in undirected graphs, the task becomes more intricate in directed graphs as it involves predicting both associations and orientations. Existing methods simply apply classic graph embedding techniques to learn node representations, followed by mapping representations of corresponding node pairs into probabilities indicating potential links. However, the inadequate capture of orientation information and sole reliance on node representations for prediction hinder the effective differentiation of orientation, thereby impeding the link prediction accuracy. In response, we introduce DGLP, an orientation-aware link prediction method tailored for directed graphs. DGLP utilizes the incidence matrix to learn both node and edge representations, effectively capturing structural and orientation information. By leveraging edge representations, DGLP achieves accurate link prediction in directed graphs without relying solely on implicit node representations. Experiments across six datasets demonstrate the effectiveness of DGLP, achieving a 1.2x improvement in prediction results.