June 10, 2024, 4:45 a.m. | Moritz Lampert, Christopher Bl\"ocker, Ingo Scholtes

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04897v1 Announce Type: new
Abstract: Dynamic link prediction is an important problem considered by many recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on publicly available 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 two issues: First, for continuous-time temporal graphs, fixed-size batches create time windows with different durations, resulting …

abstract arxiv benchmark continuous cs.lg datasets dynamic edge forecasting graph graph learning graphs however information link prediction loss patterns prediction problem show temporal type

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