图学习需摆脱低效基准，转向真实应用驱动研究
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
- 提出构建更贴近现实场景的图学习评估标准
- 指出当前基准多聚焦分子图，忽视芯片设计等关键领域
- 适合关注图神经网络实用化与跨领域落地的研究者
尽管图机器学习在药物设计和分子性质预测中展现出潜力，但严重的基准测试问题阻碍了其进一步发展。现有基准常缺乏对变革性、真实应用场景的关注，过度聚焦二维分子图等狭窄领域，而忽视组合优化、关系型数据库、芯片设计等更具影响力的方向。许多数据集未能真实反映底层数据特征，导致抽象不足且应用错配。评价体系碎片化、过度追求准确率，加剧了过拟合倾向，抑制了可泛化洞察的发展。这些局限已阻碍真正有用的图基础模型的形成。本文呼吁实现范式转变：建立更有意义的基准、严格的评估协议，并加强与领域专家的合作，推动图学习在实际应用中的可靠进步。
原文摘要 · Abstract (English)
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design. Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases. Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning.
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