Traditional Graph Neural Networks (GNNs) rely on network homophily, which can lead to performance degradation due to over-smoothing in many real-world heterophily scenarios. Recent studies analyze the smoothing effect (separability) after message-passing (MP), depending on the expectation of node features. Regarding separability gain, they provided theoretical backgrounds on over-smoothing caused by various propagation schemes, including positive, signed, and blocked MPs. More recently, by extending these theorems, some works have suggested improvements in signed propagation under multiple classes. However, prior works assume that the error ratio of all propagation schemes is fixed, failing to investigate this phenomenon correctly. To solve this problem, we propose a novel method for estimating homophily and edge error ratio, integrated with dynamic selection between blocked and signed propagation during training. Our theoretical analysis, supported by extensive experiments, demonstrates that blocking MP can be more effective than signed propagation under high edge error ratios, improving the performance in both homophilic and heterophilic graphs.
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（交通工程教育部重点实验室，同济大学）；Urban Mobility Institute, Tongji University（交通 mobility 院，同济大学）
SSHPool: The Separated Subgraph-based Hierarchical Pooling
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；School of Information, Central University of Finance and Economics（中央财经大学信息学院）；Zhejiang Normal University（浙江师范大学）；School of Cyber Science and Technology, Sun Yat-Sen University（中山大学网络空间安全学院）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of York（约克大学）
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；School of Information, Central University of Finance and Economics（中央财经大学信息学院）；Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University（浙江师范大学浙江省智能教育技术与应用重点实验室）；Department of Computer Science, University of York（约克大学计算机学院）
Semi-Supervised Learning on Graphs using Graph Neural Networks
School of Mathematical Sciences, Xiamen University（厦门大学数学科学学院）；Department of Statistics, University of Chicago（芝加哥大学统计系）；ESSEC Business School（ESSEC商学院）；Department of Applied Mathematics, University of Twente（特文特大学应用数学系）
GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University（东北大学大数据管理与分析重点实验室）；The Department of Computer Science, Aalborg University（奥尔堡大学计算机科学系）；College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学院生物信息工程学院）
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
School of Airspace Science and Engineering, Shandong University（山东大学 airspace 科学与工程学院）；Department of Computer Science, Beijing Institute of Technology（北京理工大学计算机学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
Berlin Institute for the Foundations of Learning（学习与数据基础研究院）；Machine Learning Group, Technical University of Berlin（柏林技术大学机器学习组）；Department of Artificial Intelligence, Korea University（韩国大学人工智能系）；Max Planck Institute for Informatics（信息研究所）；Department of Chemistry, Chemical Physics Theory Group, University of Toronto（多伦多大学化学系、化学物理理论组）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Acceleration Consortium, University of Toronto（多伦多大学加速联盟）
