2026拓扑深度学习挑战赛：统一评估GNN与TNN
原标题：Topological Deep Learning Challenge 2026: Bridging the Gap
AI 摘要
该论文介绍了2026年拓扑深度学习挑战赛（TDL Challenge 2026），由G. Bernárdez等人组织，在第二届TAG-DS会议上举办。挑战赛要求参赛者在TopoBench中实现图神经网络（GNN）和拓扑神经网络（TNN）的最新架构，并在GraphUniverse生成的合成基准上统一评估。最终收到来自34支团队的68份合格提交，首次引入公开排行榜；分析发现两类模型差异高度依赖任务，社区检测上平均准确率差距不大，而三角形计数上Track 1平均误差是Track 2的1.85倍。
正文节选
G. Bernárdez, L. Telyatnikov, M. Papillon et al Topological Deep Learning Challenge 2026: Bridging the Gap Abstract This paper describes the 2026 edition of the Topological Deep Learning Challenge: Bridging the Gap, hosted at the second Topology, Algebra, and Geometry in Data Science (TAG-DS) Conference. Where previous editions grew the modelling and data ecosystem of Topological Deep Learning (TDL), this edition set out to place Graph Neural Networks (GNNs) and Topological Neural Networks (TNNs