Title: Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements Show Chinese title

Title: 重新审视图神经网络在图级任务上的应用：全面实验、分析与改进

Abstract: Graphs are essential data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which predict properties or classes for the entire graph, are critical for applications, such as molecular property prediction and subgraph counting. Graph Neural Networks (GNNs) have shown promise in these tasks, but their evaluations are often limited to narrow datasets, tasks, and inconsistent experimental setups, restricting their generalizability. To address these limitations, we propose a unified evaluation framework for graph-level GNNs. This framework provides a standardized setting to evaluate GNNs across diverse datasets, various graph tasks (e.g., graph classification and regression), and challenging scenarios, including noisy, imbalanced, and few-shot graphs. Additionally, we propose a novel GNN model with enhanced expressivity and generalization capabilities. Specifically, we enhance the expressivity of GNNs through a $k$-path rooted subgraph approach, enabling the model to effectively count subgraphs (e.g., paths and cycles). Moreover, we introduce a unified graph contrastive learning algorithm for graphs across diverse domains, which adaptively removes unimportant edges to augment graphs, thereby significantly improving generalization performance. Extensive experiments demonstrate that our model achieves superior performance against fourteen effective baselines across twenty-seven graph datasets, establishing it as a robust and generalizable model for graph-level tasks. Show Chinese abstract

Abstract: 图是建模社会网络、分子结构和生物系统等领域复杂交互的重要数据结构。图级任务，即对整个图预测属性或类别，在应用中至关重要，例如分子属性预测和子图计数。图神经网络（GNNs）在这些任务中表现出色，但它们的评估通常局限于狭窄的数据集、任务和不一致的实验设置，限制了它们的泛化能力。为解决这些限制，我们提出了一个统一的图级GNN评估框架。该框架提供了一个标准化的环境，以在多种数据集、各种图任务（例如图分类和回归）以及包括噪声、不平衡和少样本图在内的挑战性场景中评估GNN。此外，我们提出了一种具有增强表达能力和泛化能力的新GNN模型。具体来说，我们通过一种$k$路径根植的子图方法增强了GNN的表达能力，使模型能够有效地计数子图（例如路径和环）。此外，我们引入了一种跨不同领域的统一图对比学习算法，该算法自适应地移除不重要的边以增强图，从而显著提高泛化性能。大量实验表明，我们的模型在二十七个图数据集上相对于十四种有效基线模型表现出优越的性能，确立了其作为图级任务的稳健且泛化能力强的模型。