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arXiv · 2406.04897

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

Moritz Lampert·Christopher Blöcker·Ingo Scholtes
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Abstract

Dynamic link prediction is an important problem considered in many recent works that propose approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on continuous-time and discrete-time temporal graph datasets, typically using a traditional batch-oriented evaluation setup. However, as we show in this work, a batch-oriented evaluation is often unsuitable and can cause several issues. Grouping edges into fixed-sized batches regardless of their occurrence time leads to information loss or leakage, depending on the temporal granularity of the data. Furthermore, fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. In this work, we empirically show how traditional batch-based evaluation 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.

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Moritz Lampert, Christopher Blöcker, Ingo Scholtes. 2024-06-07. From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning. https://arxiv.org/abs/2406.04897

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