Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields of science. To systematically address this, we introduce ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilities of GNNs in handling very long-range graph propagation. ECHO includes three synthetic graph tasks, namely single-source shortest paths, node eccentricity, and graph diameter, each constructed over diverse and structurally challenging topologies intentionally designed to introduce significant information bottlenecks. ECHO also includes two real-world datasets, ECHO-Charge and ECHO-Energy, which define chemically grounded benchmarks for predicting atomic partial charges and molecular total energies, respectively, with reference computations obtained at the density functional theory (DFT) level. Both tasks inherently depend on capturing complex long-range molecular interactions. Our extensive benchmarking of popular GNN architectures reveals clear performance gaps, emphasizing the difficulty of true long-range propagation and highlighting design choices capable of overcoming inherent limitations. ECHO thereby sets a new standard for evaluating long-range information propagation, also providing a compelling example for its need in AI for science.

翻译：有效捕捉长程相互作用仍然是图神经网络（GNN）研究中一个基本但尚未解决的挑战，这对跨科学各领域的应用至关重要。为系统性地解决这一问题，我们提出了ECHO（长跳通信评估），这是一个专门设计用于严格评估GNN处理超长程图传播能力的新型基准。ECHO包含三项合成图任务，即单源最短路径、节点偏心距和图直径，每项任务均构建于多样且结构复杂的拓扑之上，这些拓扑被特意设计以引入显著的信息瓶颈。ECHO还包含两个真实世界数据集：ECHO-Charge与ECHO-Energy，它们分别定义了基于化学原理的原子部分电荷预测和分子总能量预测基准，其参考计算均在密度泛函理论（DFT）层面获得。这两项任务本质上都依赖于捕捉复杂的长程分子相互作用。我们对主流GNN架构的广泛基准测试揭示了明显的性能差距，强调了实现真正长程传播的难度，同时指出了能够克服固有局限性的设计选择。因此，ECHO为评估长程信息传播设立了新标准，也为科学人工智能领域对此类基准的需求提供了有力例证。