Rethinking Evaluation for Temporal Link Prediction through Counterfactual Analysis
Track: long paper (up to 4 pages)
Keywords: temporal link prediction, graph learning
TL;DR: What if a temporal link prediction model is tested on a temporally distorted version of the data instead of the real data?
Abstract: In response to critiques of existing evaluation methods for temporal link prediction (TLP) models, we propose a novel approach to verify if these models truly capture temporal patterns in the data. Our method involves a sanity check formulated as a counterfactual question: ``What if a TLP model is tested on a temporally distorted version of the data instead of the real data?'' Ideally, a TLP model that effectively learns temporal patterns should perform worse on temporally distorted data compared to real data. We analyse this hypothesis and introduce two temporal distortion techniques to assess six well-known TLP models.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Submission Number: 4
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