Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data, referred to as over-fitting. This paper proposes Topology Adaptive Edge Dropping (TADropEdge) method as an adaptive data augmentation technique to improve generalization performance and learn robust GNN models. We start by explicitly analyzing how random edge dropping increases the data diversity during training, while indicating i.i.d. edge dropping does not account for graph structural information and could result in noisy augmented data degrading performance. To overcome this issue, we consider graph connectivity as the key property that captures graph topology. TADropEdge incorporates this factor into random edge dropping such that the edge-dropped subgraphs maintain similar topology as the underlying graph, yielding more satisfactory data augmentation. In particular, TADropEdge first leverages the graph spectrum to assign proper weights to graph edges, which represent their criticality for establishing the graph connectivity. It then normalizes the edge weights and drops graph edges adaptively based on their normalized weights. Besides improving generalization performance, TADropEdge reduces variance for efficient training and can be applied as a generic method modular to different GNN models. Intensive experiments on real-life and synthetic datasets corroborate theory and verify the effectiveness of the proposed method.
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学医学院）
Online Graph Topology Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation
Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦校区电子与电气工程系）；National Heart and Lung Institute, Imperial College London（帝国理工学院伦敦校区国家心脏和肺研究所）
Robustness in sparse artificial neural networks trained with adaptive topology
Dept. of Biological Physics, Eötvös Loránd University（生物物理学系，欧文·洛朗大学）；Semmelweis University, Faculty of Health and Public Administration, Health Services Management Training Centre（塞梅利维斯大学，公共健康与行政学院，卫生服务管理培训中心）；Center for Complex Networks and Systems Research（复杂网络与系统研究所以）；Luddy School of Informatics, Computing, and Engineering（信息学、计算与工程学院）；Indiana University Bloomington（印第安纳大学布卢明顿分校）
SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
University of California, Los Angeles（加州大学洛杉矶分校）；University of California, Riverside（加州大学河滨分校）；U.S. Army DEVCOM Army Research Laboratory（美国陆军 DEVCOM 军事研究实验室）
Scale-Adaptive Power Flow Analysis with Local Topology Slicing and Multi-Task Graph Learning
School of Electric Power, South China University of Technology（华南理工大学电力学院）；CSG Energy Development Research Institute Co., Ltd.（广东电网公司电力发展研究院有限公司）；Electric Power Research Institute of China Southern Power Grid Company Limited（中国南方电网公司电力研究院）
Spectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
Department of Computer Science Virginia Commonwealth University Richmond, VA, USA（计算机科学系弗吉尼亚 Commonwealth 大学里士满, VA, 美国）；Department of Computer Science The University of Alabama at Birmingham Birmingham, AL, USA（计算机科学系阿拉巴马大学伯明翰分校伯明翰, AL, 美国）
Graph Contrastive Learning with Adaptive Augmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Alibaba Group（阿里巴巴集团）
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；CNRS（法国国家科学研究中心）；Paris University（巴黎大学）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Jiangxi Normal University（江西师范大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯人工智能实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Fudan University（复旦大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）
