呼吁构建更高阶拓扑数据集，推动几何深度学习发展
Have Graph -- Will Lift? The Case for Higher-Order Benchmarks
- 提出从现有图数据升维构造高阶数据集的局限性
- 强调需主动创建新数据集以支撑拓扑深度学习研究
- 适合关注几何深度学习、拓扑机器学习的研究者
尽管几何与拓扑在机器学习中已初具影响力，但模型多样性与可用基准数据集稀缺形成鲜明对比。当前研究多依赖将已有图数据升维以引入高阶信息，但这难以充分反映真实复杂结构。本文主张社区应主动构建新的高阶数据集，为拓扑深度学习奠定更坚实的基础。该方向对提升模型归纳偏置的合理性具有重要意义。
原文摘要 · Abstract (English)
After a somewhat rocky start, geometry and topology have established a foothold in machine learning. Message passing, either on graphs or higher-order complexes, is one of the main drivers of geometric deep learning, and paradigms that were once considered to be firmly in the realm of the abstract-like sheaves-have been "tamed" to serve as novel inductive biases for model architectures in topological deep learning. The veritable diversity of models, however, is in stark contrast to the scarcity of suitable benchmark datasets. As a result, researchers often resort to lifting existing graph datasets to include higher-order information. In this opinion paper, I want to encourage the community to also source new datasets, which may be used to prop up the foundations of our research field.
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