The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for prediction tasks relying on long-range interactions, recursive aggregation of node features can lead to an undesired phenomenon called "oversquashing". We present a framework for analyzing oversquashing based on information contraction. Our analysis is guided by a model of reliable computation due to von Neumann that lends a new insight into oversquashing as signal quenching in noisy computation graphs. Building on this, we propose a graph rewiring algorithm aimed at alleviating oversquashing. Our algorithm employs a random local edge flip primitive motivated by an expander graph construction. We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy. Synthetic experiments show that while our algorithm in general has a slower rate of expansion, it is overall computationally cheaper, preserves the node degrees exactly and never disconnects the graph.
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Department of Computer Science, Georgia State University（佐治亚州立大学计算机科学系）；Department of Data Science and Analytics, Kennesaw State University（肯尼索州立大学数据科学与分析系）
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
Technion - Israel Institute of Technology, Faculty of Mathematics（技术离子-以色列理工学院，数学系）；Technical University of Munich, School of Computation, Information and Technology（慕尼黑技术大学，计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Leadership Computing Facility, Argonne National Laboratory（阿贡国家实验室领导计算设施）；Department of Statistics, University of Chicago（芝加哥大学统计系）；NSF-Simons National Institute for Theory and Mathematics in Biology（国家理论与生物数学研究所）
本文提出将随机化全局嵌入（Sketched Random Features）注入标准图神经网络，以缓解过度挤压、过度平滑和表达力受限问题，从而高效捕获长程依赖并在真实图任务上持续提升性能。
SCNode: Spatial and Contextual Coordinates for Graph Representation Learning
Department of Mathematical Science（数学科学系）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Department of Mathematics（数学系）；Florida State University（佛罗里达州立大学）；Department of Computer Science（计算机科学系）；AI Initiative（人工智能计划）；University of Central Florida（中央佛罗里达大学）
Weizmann Institute of Science（魏茨曼科学研究所）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；NVIDIA Research（英伟达研究院）；Meta AI Research Centre for Artificial Intelligence（Meta人工智能研究中心）
Probing Graph Neural Network Activation Patterns Through Graph Topology
Department of Computer Science, University of Geneva, Geneva, Switzerland（日内瓦大学计算机科学系）；Research Institute for Statistics and Information Science, Geneva, Switzerland（日内瓦统计与信息科学研究所）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Department of Excellence L’EMbeDS, Sant’Anna School of Advanced Studies, Pisa, Italy（卓越部门L’EMbeDS，圣安娜高级研究学院，意大利比萨）；Department of Computer Science, University of Pisa, Italy（计算机科学系，意大利比萨大学）；Department of Statistics, The Pennsylvania State University, USA（统计系，美国宾夕法尼亚州立大学）；Huck Institutes of the Life Sciences, The Pennsylvania State University, USA（生命科学学院Huck研究所，美国宾夕法尼亚州立大学）；IMT School for Advanced Studies, Lucca, Italy（IMT高级研究学院，意大利卢卡）；Computational Network Science, RWTH Aachen University, Aachen, Germany（计算网络科学，德国亚琛工业大学，亚琛）；DTU Technical University of Denmark, Lyngby, Denmark（丹麦技术大学DTU，Lyngby，丹麦）
A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
Department of Computing Sciences, Bocconi University, Milan, Italy（计算机科学系，博科尼大学，米兰，意大利）；Bocconi Institute for Data Science and Analytics (BIDSA)（博科尼数据科学与分析研究所）
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
Department of Computer Science, Rice University（计算机科学系，里士大学）；Rice Advanced Materials Institute, Rice University（里士大学先进材料研究所）；Department of Materials Science and Nanoengineering, Rice University（材料科学与纳米工程系，里士大学）；Thayer School of Engineering, Dartmouth College（达特茅斯学院泰勒工程学院）；Department of Computer Sciences, University of Wisconsin–Madison（威斯康星大学麦迪逊分校计算机科学系）
