头回有人把Sheaf神经网络放到归纳任务里系统跑分，1890组实验，结论是调外围架构比调Sheaf算子更划算。
该论文首次系统评测Sheaf Neural Networks在归纳学习下的表现，覆盖三种扩散机制、三种限制图参数化和三种茎维度。研究采用不组装重Sheaf拉普拉斯算子的消息传递重构，支持跨图批处理。作者在14个归纳数据集上完成1890组受控实验，发现限制图是主导设计选择，通用映射优于特殊映射。实验还显示，增大茎维度提升容量但无法增强长程信息传递，架构组件对性能差异的贡献超过Sheaf特定设计空间本身。匹配协议下SNN可迁移至归纳场景，但与最强基线仍有数据集相关的差距。
Benchmarking Sheaf Neural Networks for Inductive Tasks
Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-dependent restriction maps between node stalks. Despite their strong theoretical foundations and promising transductive results, SNNs have been evaluated almost exclusively on transductive node classification, leaving their behaviour under inductive protocols unknown. We address this gap through the first systematic benchmark of the sheaf design space, evaluating three diffusion mechanisms (neural sheaf diffusion, sheaf attention, and sheaf attention with Graph Attention Network v2), three restriction-map parameterizations, three stalk dimensions, and six modern GNN architectural components, within a message-passing reformulation that never assembles the heavy sheaf Laplacian, making the full design space trainable under cross-graph batching. Across $1{,}890$ controlled experiments on 14 inductive datasets, multiple insights emerge: restriction maps are the dominant design choice and general maps are preferable, larger stalks add capacity but not long-range reach, architectural components explain more performance variation than the entire sheaf-specific design space itself. Under a matched protocol, SNNs transfer to inductive settings but do not reach the strongest baselines, with gaps being dataset-dependent. Practically, a single sheaf configuration can generalize across datasets, so effort is better spent tuning the surrounding architectural recipe than the sheaf operator itself.