简单启发式方法在时间图上表现堪比甚至超过顶尖神经网络模型。
On the Power of Heuristics in Temporal Graphs
- 仅利用新近性和流行性两种时间模式设计启发式方法。
- 在BenchTemp和TGB数据集上达到或超越当前最优性能。
- 适合关注时间图建模公平评估与可复现性的研究者。
动态图数据常表现出显著的时间模式，如新近性（优先考虑近期交互）和流行性（偏好频繁出现的节点）。我们发现，仅利用这些模式的简单启发式方法，在标准评估协议下表现可媲美甚至优于最先进的神经网络模型。为深入探究这些动态特性，我们引入了量化新近性和流行性影响的指标。在BenchTemp和时间图基准（Temporal Graph Benchmark）上的实验表明，我们的方法在后者所有数据集上达到最先进水平，并在前者多个数据集上位列榜首。结果强调了改进评估方案的重要性，以实现公平比较并推动更鲁棒的时间图模型发展。此外，揭示了当前深度学习方法往往难以捕捉真实时间图预测中的关键模式。代码已公开以确保可复现性。
原文摘要 · Abstract (English)
Dynamic graph datasets often exhibit strong temporal patterns, such as recency, which prioritizes recent interactions, and popularity, which favors frequently occurring nodes. We demonstrate that simple heuristics leveraging only these patterns can perform on par or outperform state-of-the-art neural network models under standard evaluation protocols. To further explore these dynamics, we introduce metrics that quantify the impact of recency and popularity across datasets. Our experiments on BenchTemp and the Temporal Graph Benchmark show that our approaches achieve state-of-the-art performance across all datasets in the latter and secure top ranks on multiple datasets in the former. These results emphasize the importance of refined evaluation schemes to enable fair comparisons and promote the development of more robust temporal graph models. Additionally, they reveal that current deep learning methods often struggle to capture the key patterns underlying predictions in real-world temporal graphs. For reproducibility, we have made our code publicly available.
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