Message passing is the dominant paradigm in Graph Neural Networks (GNNs). The efficiency of message passing, however, can be limited by the topology of the graph. This happens when information is lost during propagation due to being oversquashed when travelling through bottlenecks. To remedy this, recent efforts have focused on graph rewiring techniques, which disconnect the input graph originating from the data and the computational graph, on which message passing is performed. A prominent approach for this is to use discrete graph curvature measures, of which several variants have been proposed, to identify and rewire around bottlenecks, facilitating information propagation. While oversquashing has been demonstrated in synthetic datasets, in this work we reevaluate the performance gains that curvature-based rewiring brings to real-world datasets. We show that in these datasets, edges selected during the rewiring process are not in line with theoretical criteria identifying bottlenecks. This implies they do not necessarily oversquash information during message passing. Subsequently, we demonstrate that SOTA accuracies on these datasets are outliers originating from sweeps of hyperparameters -- both the ones for training and dedicated ones related to the rewiring algorithm -- instead of consistent performance gains. In conclusion, our analysis nuances the effectiveness of curvature-based rewiring in real-world datasets and brings a new perspective on the methods to evaluate GNN accuracy improvements.
The Institute for Artificial Intelligence of Serbia（塞尔维亚人工智能研究所）；McGill University（麦吉尔大学）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Google DeepMind（谷歌DeepMind）
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）
Gradient Rewiring for Editable Graph Neural Network Training
Texas A&M University（德克萨斯农工大学）；University of Minnesota（明尼苏达大学）；Case Western Reserve University（凯斯西储大学）；NYU Shanghai（上海纽约大学）；North Carolina State University（北卡罗来纳州立大学）；University of Houston（休斯顿大学）；Rice University（莱斯大学）
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
Xidian University（西安电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Chu Hai College（香港 Chu Hai 学院）；National University of Defense Technology（国防科技大学）；Shanghai JiaoTong University（上海交通大学）；Hubei University（湖北大学）
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
FPT Software AI Center（FPT软件人工智能中心）；University of Science, Vietnam National University Ho Chi Minh City（越南国家大学胡志明市分校科学大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Los Angeles（加州大学洛杉矶分校）；National University of Singapore（新加坡国立大学）
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
Télécom Paris - Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院）；BLUAI；La Rochelle Université（拉罗谢尔大学）；Université Paris-Saclay（巴黎萨克雷大学）；CentraleSupélec（中央理工-高等电力学院）；Inria（法国国家信息与自动化研究所）
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
Institute of Innovation, Science, and Sustainability（创新、科学与可持续性研究所）；Federation University Australia（联邦大学澳大利亚分校）；Stainaz Research（斯泰纳兹研究）；MIoT & IPIN Lab（MIoT与IPIN实验室）；University of New South Wales（新南威尔士大学）；School of Information and Electronic Engineering（信息与电子工程学院）；Zhejiang Gongshang University（浙江工商大学）；School of Computing Technologies（计算技术学院）
