分子拓扑剖面（MOLTOP）——分子图分类的简单且强基线
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification
AI总结：
本文提出了一种名为MOLTOP的简单且无需超参数的分子图分类基线算法，通过结合边描述符直方图聚合与随机森林，在多个基准数据集上展现出与现代GNNs相当的竞争力，强调了拓扑描述符基线对准确评估GNN进展的重要性。
摘要：
我们重新审视了拓扑描述符在分子图分类中的有效性，并设计了一个简单却强大的基线。我们证明了一种简单的特征工程方法——对边描述符采用直方图聚合，对原子序数和键类型采用独热编码——当与随机森林分类器结合时，能为图神经网络（GNNs）建立强大的基线。这种名为分子拓扑剖面（MOLTOP）的新算法，集成了边介数中心性、调整兰德指数和SCAN结构相似性得分。与现代GNNs相比，该方法极具竞争力，同时具备简单、快速、低方差和无需超参数的特点。我们的方法在MoleculeNet数据集上使用Open Graph Benchmark提供的公平评估协议进行了严格测试。我们还展示了在Long Range Graph Benchmark的肽分类任务上的域外生成能力。跨越11个基准数据集的评估揭示了MOLTOP强大的判别能力，它超越了1-WL测试，对于某些类别的图甚至超越了3-WL测试。我们的结论是，基于描述符的基线（如我们提出的这一种）对于准确评估GNN领域的进展仍然至关重要。
英文摘要：
We revisit the effectiveness of topological descriptors for molecular graph classification and design a simple, yet strong baseline. We demonstrate that a simple approach to feature engineering - employing histogram aggregation of edge descriptors and one-hot encoding for atomic numbers and bond types - when combined with a Random Forest classifier, can establish a strong baseline for Graph Neural Networks (GNNs). The novel algorithm, Molecular Topological Profile (MOLTOP), integrates Edge Betweenness Centrality, Adjusted Rand Index and SCAN Structural Similarity score. This approach proves to be remarkably competitive when compared to modern GNNs, while also being simple, fast, low-variance and hyperparameter-free. Our approach is rigorously tested on MoleculeNet datasets using fair evaluation protocol provided by Open Graph Benchmark. We additionally show out-of-domain generation capabilities on peptide classification task from Long Range Graph Benchmark. The evaluations across eleven benchmark datasets reveal MOLTOP's strong discriminative capabilities, surpassing the $1$-WL test and even $3$-WL test for some classes of graphs. Our conclusion is that descriptor-based baselines, such as the one we propose, are still crucial for accurately assessing advancements in the GNN domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
