图变压器在脑连接组分类中表现不如传统GNN，且边信息影响有限。
On the Limits of Applying Graph Transformers for Brain Connectome Classification
- 用图变压器与传统GNN对比，测试其在脑连接组数据上的表现
- 即使移除全部边，模型准确率仍保持稳定，表明图结构作用弱
- 提醒需更严谨的数据清洗和预处理，才能发挥图结构优势
脑连接组提供了大脑神经连接的详细图谱。近期研究提出了新的连接组图数据集，并尝试通过图深度学习提升分类性能。随着变换器在建模复杂关系方面展现出优越性，本文评估其在NeuroGraph基准数据集及模拟噪声数据（通过概率性移除边生成）上的表现。结果表明，图变压器在该数据集上并未显著优于传统图神经网络（GNN）。此外，无论是传统GNN还是变压器型GNN，在所有边被移除后仍保持较高分类准确率，暗示该数据集的图结构对预测贡献较小。研究建议进一步评估NeuroGraph作为脑连接组基准的有效性，强调需建立更精细的数据集与改进预处理策略，以确保边连接具有实际意义。
原文摘要 · Abstract (English)
Brain connectomes offer detailed maps of neural connections within the brain. Recent studies have proposed novel connectome graph datasets and attempted to improve connectome classification by using graph deep learning. With recent advances demonstrating transformers' ability to model intricate relationships and outperform in various domains, this work explores their performance on the novel NeuroGraph benchmark datasets and synthetic variants derived from probabilistically removing edges to simulate noisy data. Our findings suggest that graph transformers offer no major advantage over traditional GNNs on this dataset. Furthermore, both traditional and transformer GNN models maintain accuracy even with all edges removed, suggesting that the dataset's graph structures may not significantly impact predictions. We propose further assessing NeuroGraph as a brain connectome benchmark, emphasizing the need for well-curated datasets and improved preprocessing strategies to obtain meaningful edge connections.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。