Summary of From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning, by Moritz Lampert et al.
From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning
by Moritz Lampert, Christopher Blöcker, Ingo Scholtes
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper examines the effectiveness of various approaches for learning temporal edge patterns in dynamic link prediction, a crucial problem in recent works. To assess their efficacy, models are typically evaluated on benchmark datasets involving continuous-time and discrete-time temporal graphs. However, the authors show that common batch-oriented evaluation can cause multiple issues depending on the datasets’ characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this work, the suitability of common batch-oriented evaluation is assessed for its impact on dynamic link prediction tasks in both continuous-time and discrete-time temporal graphs. The authors demonstrate that fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. Additionally, the sequence of batches can introduce temporal dependencies not present in the data. |
