Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation

• URL: http://arxiv.org/abs/2512.17762v1
• Date: Fri, 19 Dec 2025 16:34:27 GMT
• Title: Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
• Authors: Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu,
• Abstract summary: ECHO is a benchmark designed to rigorously assess the capabilities of graph neural networks (GNNs) in handling very long-range graph propagation.<n> ECHO includes three synthetic graph tasks, namely single-source shortest paths, node eccentricity, and graph diameter.<n> 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.
• Score: 21.58194886553004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Spiking Heterogeneous Graph Attention Networks [17.094622281945853]
  We propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN) to reduce the computing cost without compromising the performance.<n>SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters.<n>It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation.
  arXiv  Detail & Related papers  (2025-12-31T08:17:59Z)
• Topological Feature Compression for Molecular Graph Neural Networks [0.0]
  We introduce a novel Graph Neural Network (GNN) architecture that combines compressed higher-order topological signals with standard molecular features.<n>Our approach captures global geometric information while preserving computational tractability and human-interpretable structure.
  arXiv  Detail & Related papers  (2025-08-11T09:45:47Z)
• ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion [73.85920403511706]
  We propose ScaleGNN, a novel framework that adaptively fuses multi-hop node features for scalable and effective graph learning.<n>We show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-22T14:05:11Z)
• DeltaGNN: Graph Neural Network with Information Flow Control [5.563171090433323]
  Graph Neural Networks (GNNs) are designed to process graph-structured data through neighborhood aggregations in the message passing process.<n>Message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing.<n>We propose a mechanism called emph information flow control to address over-smoothing and over-squashing with linear computational overhead.<n>We benchmark our model across 10 real-world datasets, including graphs with varying sizes, topologies, densities, and homophilic ratios, showing superior performance
  arXiv  Detail & Related papers  (2025-01-10T14:34:20Z)
• Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel [48.6168145845412]
  We propose a method to abstract the collective information of atomic groups into a few $textitNeural Atoms$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop.
  arXiv  Detail & Related papers  (2023-11-02T14:44:50Z)
• Simple and Efficient Heterogeneous Graph Neural Network [55.56564522532328]
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)
  arXiv  Detail & Related papers  (2022-07-06T10:01:46Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction [8.02401104726362]
  The study of graph neural network (GNN) has attracted much attention and achieved promising performance in molecular property prediction. We focus on the impact of selecting two types of GNN hyper parameters, those belonging to graph-related layers and those of task-specific layers, on the performance of GNN for molecular property prediction.
  arXiv  Detail & Related papers  (2021-04-13T09:21:27Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Hierarchical Message-Passing Graph Neural Networks [12.207978823927386]
  We propose a novel Hierarchical Message-passing Graph Neural Networks framework. Key idea is generating a hierarchical structure that re-organises all nodes in a flat graph into multi-level super graphs. We present the first model to implement this framework, termed Hierarchical Community-aware Graph Neural Network (HC-GNN)
  arXiv  Detail & Related papers  (2020-09-08T13:11:07Z)
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
  We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information. We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN) Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
  arXiv  Detail & Related papers  (2020-07-05T08:16:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.