用简单结构编码提升动态链接预测性能，提供可靠基准。
HyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encoding
- 通过相对结构编码捕捉事件序列中的模式
- 在多个基准上表现媲美复杂模型
- 适合想快速验证新方法的研究者
连续时间动态图的表示学习对动态链接预测至关重要。尽管近期方法日趋复杂，但领域内缺乏强而有意义的基线来可靠评估进展。本文提出 HyperEvent，一种通过直观编码机制捕捉事件序列中相对结构模式的简单方法。作为基础方案，HyperEvent利用相对结构编码识别有意义的事件序列，无需复杂参数化。结合轻量级 Transformer 分类器，将链接预测重构为事件结构识别任务。尽管结构简单，HyperEvent 在多个基准测试中仍取得具有竞争力的结果，常与更复杂模型表现相当。该工作表明，有效建模可通过简单的结构编码实现，为未来进步提供了清晰的评估参考。
原文摘要 · Abstract (English)
Learning representations for continuous-time dynamic graphs is critical for dynamic link prediction. While recent methods have become increasingly complex, the field lacks a strong and informative baseline to reliably gauge progress. This paper proposes HyperEvent, a simple approach that captures relative structural patterns in event sequences through an intuitive encoding mechanism. As a straightforward baseline, HyperEvent leverages relative structural encoding to identify meaningful event sequences without complex parameterization. By combining these interpretable features with a lightweight transformer classifier, HyperEvent reframes link prediction as event structure recognition. Despite its simplicity, HyperEvent achieves competitive results across multiple benchmarks, often matching the performance of more complex models. This work demonstrates that effective modeling can be achieved through simple structural encoding, providing a clear reference point for evaluating future advancements.
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