Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
Beijing University of Technology（北京工业大学）；Beijing Institute of Artificial Intelligence（北京人工智能研究院）；Beijing University of Chemical Technology（北京化工大学）；The University of Sydney（悉尼大学）
Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of valuable high-frequency information in graph signals. These issues are particularly pronounced in heterophilic graphs. To address these challenges, we propose Dual-Frequency Filtering Self-aware Graph Neural Networks (DFGNN). DFGNN integrates low-pass and high-pass filters to extract smooth and detailed topological features, using frequency-specific constraints to minimize noise and redundancy in the respective frequency bands. The model dynamically adjusts filtering ratios to accommodate both homophilic and heterophilic graphs. Furthermore, DFGNN mitigates interference by aligning topological and attribute representations through dynamic correspondences between their respective frequency bands, enhancing overall model performance and expressiveness. Extensive experiments conducted on benchmark datasets demonstrate that DFGNN outperforms state-of-the-art methods in classification performance, highlighting its effectiveness in handling both homophilic and heterophilic graphs.
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Alibaba Group（阿里巴巴集团）；Michigan State University（密歇根州立大学）
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophily in node labels, i.e., nodes with similar labels are connected in the graphs. They fail to generalize to heterophilic graphs where linked nodes may have dissimilar labels and attributes. Therefore, in this paper, we investigate a novel framework that performs well on graphs with either homophily or heterophily. More specifically, we propose a label-wise message passing mechanism to avoid the negative effects caused …
The Australian National University（澳大利亚国立大学）；Pohang University of Science and Technology（浦项科技大学）
Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in local neighborhoods. This assumption however limits the generalizability power of GNNs. To address this limitation, we propose a flexible GNN model, which is capable of handling any graphs without being restricted by their underlying homophily. At its core, this model adopts a node attention mechanism based on multiple learnable spectral filters; therefore, the aggregation scheme is learned adaptively for eac…
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Harbin Institute of Technology（哈尔滨工业大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Pengcheng Laboratory（鹏城实验室）；Fudan University（复旦大学）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Griffith University（格里菲斯大学）
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Represe…
Indian Institute of Information Technology Kottayam (IIITK)（印度信息技术学院科塔亚姆分校（IIITK））；International Institute of Information Technology Hyderabad (IIITH)（印度信息技术学院海得拉巴分校（IIITH））
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously. However, most GNNs implicitly assume that the labels of nodes and their neighbors in a graph are the same or consistent, which does not hold in heterophilic graphs, where the labels of linked nodes are likely to differ. Hence, when the topology is non-informative for label prediction, ordinary GNNs may work significantly worse than simply applying multi-layer perceptrons (MLPs) on…
HeRB: Heterophily-Resolved Structure Balancer for Graph Neural Networks
Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications（江苏大数据安全与智能处理重点实验室，南京邮电大学）；School of Computer Science, Nanjing University of Posts and Telecommunications（计算机科学学院，南京邮电大学）；State Key Laboratory of Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）
针对图神经网络的结构不平衡问题，提出 HeRB 方法，先纠正异相性再传递同相知识，通过减少类间边和增加类内边及头尾节点知识传递，在多个基准数据集上取得优越性能。
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Huawei Technologies Co., Ltd（华为技术有限公司）
Renmin University of China（中国人民大学）；Alibaba Group（阿里巴巴集团）
Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels. Although many methods have been proposed to improve the robustness of GNN models, most of these techniques are restricted to the spatial domain and employ complicated de…
Graph Neural Networks for Graphs with Heterophily: A Survey
School of Computing Technologies, RMIT University（罗特学院，皇家墨尔本理工大学）；Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）；Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University（浙江智能教育技术与应用重点实验室，浙江师范大学）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术学院，哈尔滨工业大学（深圳））；School of Computer Science and Technology, Tianjin University（计算机科学与技术学院，天津大学）；Department of Computer Science, University of Illinois at Chicago（计算机科学系，伊利诺伊大学芝加哥分校）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Purdue University（普渡大学）
In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also universal. Here, universality refers to independence on homophily or heterophily graph assumptions. We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological…
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
National University of Singapore（新加坡国立大学）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
Hong Kong Baptist University（香港浸会大学）；Renmin University of China（中国人民大学）；Chinese Academy of Sciences（中国科学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Sun Yat-sen University（中山大学）
Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞斯泰大学数学与计算机科学学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）
Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective
Beijing University of Posts and Telecommunications（北京邮电大学）；National University of Singapore（新加坡国立大学）；Beihang University（北京航空航天大学）；Hangzhou Dianzi University（杭州电子科技大学）
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with limited benchmark datasets. As a result, the existing evaluation of GNNs lacks fine-grained analysis from various characteristics of graphs. Motivated by this, we conduct extensive experiments with a synthetic graph generator that can generate graphs having controlled characteristics for fine-grained analysis. Our empirical studies clarify the strengths and weaknesses of GNNs from four major characteristics of real-wo…
