Link Prediction in Dynamic Graphs for Recommendation

Recent advances in employing neural networks on graph domains helped push the state of the art in link prediction tasks, particularly in recommendation services. However, the use of temporal contextual information, often modeled as dynamic graphs that encode the evolution of user-item relationships over time, has been overlooked in link prediction problems. In this paper, we consider the hypothesis that leveraging such information enables models to make better predictions, proposing a new neural network approach for this. Our experiments, performed on the widely used ML-100k and ML-1M datasets, show that our approach produces better predictions in scenarios where the pattern of user-item relationships change over time. In addition, they suggest that existing approaches are significantly impacted by those changes.Comment: Workshop on Relational Representation Learning (R2L), NIPS 201

Fast Evaluation of Link Prediction by Random Sampling of Unobserved Links

The evaluation of a link prediction algorithm requires to estimate the possibility of the existence of all unobserved links in a network. However, the number of unobserved links grows exponentially with the increase of the number of nodes, which limits link prediction in large networks. In this paper, we propose a new evaluation scheme for link prediction algorithms, i.e., link prediction with random sampling. We use this method to evaluate the performance of twelve link predictors on ten real-world networks of different contexts and scales. The results show that the performance ranking of these algorithms is not affected by randomly sampling a very small part from unobserved links for experiments, whether using AUC or the precision metric. Moreover, this sampling method can reduce the computational complexity for the evaluation of link prediction algorithms from O(n^2) to O(n) in large networks. Our findings show that the proposed scheme is a fast and effective evaluation method