From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning

Abstract

Dynamic link prediction is an important problem often considered in recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on benchmark datasets involving continuous-time and discrete-time temporal graphs. However, as we show in this work, the suitability of common batch-oriented evaluation depends on the datasets' characteristics, which can cause multiple issues: For continuous-time temporal graphs, fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. For discrete-time temporal graphs, the sequence of batches can additionally introduce temporal dependencies that are not present in the data. In this work, we empirically show that this common evaluation approach leads to skewed model performance and hinders the fair comparison of methods. We mitigate this problem by reformulating dynamic link prediction as a link forecasting task that better accounts for temporal information present in the data.