Link Prediction Based on Heuristics and Graph Attention

Recent years have seen a surge in many deep learning research approaches to predict links in structured network data, however, these approaches seem to falter in its applicability. This paper seeks to propose a new model, named HLPGAM (Heuristics Link Prediction Graph Attention Mechanism), which combines probabilistic heuristics and attention mechanism to learn a more suitable way of predicting links in a given structured-network without relying on sophisticated feature engineering based on the statistical properties of a given node. The paper first aligns graphs and performs graph2Vec conversion using graph convolutions operation then it overcomes entity classification and link prediction limitation via an attention mechanism, i.e. to replace the normalization with data-dependent attention weights. For the entity classification problem, the experimental results have demonstrated that the HLP-GAM model can act as a competitive, end-to-end trainable graph-based encoder. For link prediction, the HLP-GAM model outperformed direct optimization of the factorization model and achieved competitive results on standard link prediction benchmarks. Our model achieves much better performance than other algorithms when he experimented both based on AIFB and AM dataset.