Link prediction heuristics for temporal graph benchmark

Link prediction is one of the most well-known and studied problems in graph machine learning, successfully applied in different settings, such as predicting network evolution in online social networks, protein-to-protein interactions, or completing links in knowledge graphs. In recent years, we have witnessed several solutions based on deep learning methods for solving this task in the context of temporal networks. However, despite their effectiveness on static graphs, traditional heuristic-based approaches from network science research have never been considered potential benchmarks' baselines. For this reason, in this work, we tested four of the most well-known and simple heuristics for link prediction on the most adopted temporal graph benchmark (TGB). Our results show that simple link prediction heuristics can reach comparable results with state-of-the-art deep learning techniques and, thanks to their interpretability, give insights into the network being studied. We believe considering heuristic-based baselines will push the temporal graph learning community toward better models for link prediction.