{"total":95,"rows":[{"id":"2302.11640","title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","created":"2023-02-22","cited_by_count":343,"area":null},{"id":"2205.07308","title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily","created":"2022-05-15","cited_by_count":179,"area":null},{"id":"2202.07082","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","created":"2022-02-14","cited_by_count":175,"area":null},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133,"area":null},{"id":"2302.01524","title":"Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing","created":"2023-02-03","cited_by_count":67,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2209.06177","title":"Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond","created":"2022-09-13","cited_by_count":60,"area":null},{"id":"2211.14065","title":"Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating","created":"2022-11-25","cited_by_count":57,"area":null},{"id":"2111.06748","title":"Simplifying approach to Node Classification in Graph Neural Networks","created":"2021-11-12","cited_by_count":56,"area":null},{"id":"2211.14208","title":"GREAD: Graph Neural Reaction-Diffusion Networks","created":"2022-11-25","cited_by_count":54,"area":null},{"id":"2310.18884","title":"Simple and Asymmetric Graph Contrastive Learning without Augmentations","created":"2023-10-29","cited_by_count":38,"area":null},{"id":"2403.16030","title":"VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections","created":"2024-03-24","cited_by_count":34,"area":null},{"id":"2312.14438","title":"PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering","created":"2023-12-22","cited_by_count":28,"area":null},{"id":"2405.12474","title":"How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing","created":"2024-05-21","cited_by_count":24,"area":null},{"id":"2302.12357","title":"Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs","created":"2023-02-23","cited_by_count":23,"area":null},{"id":"2207.11311","title":"Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective","created":"2022-07-22","cited_by_count":19,"area":null},{"id":"2210.17224","title":"Improving Graph Neural Networks with Learnable Propagation Operators","created":"2022-10-31","cited_by_count":19,"area":null},{"id":"2306.04265","title":"Permutation Equivariant Graph Framelets for Heterophilous Graph Learning","created":"2023-06-07","cited_by_count":19,"area":null},{"id":"2211.14383","title":"Interpreting Unfairness in Graph Neural Networks via Training Node Attribution","created":"2022-11-25","cited_by_count":18,"area":null},{"id":"2202.04139","title":"Simplified Graph Convolution with Heterophily","created":"2022-02-08","cited_by_count":17,"area":null},{"id":"2212.03654","title":"Node-oriented Spectral Filtering for Graph Neural Networks","created":"2022-12-07","cited_by_count":17,"area":null},{"id":"2406.03464","title":"Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach","created":"2024-06-05","cited_by_count":17,"area":"moe"},{"id":"2307.16092","title":"Feature Transportation Improves Graph Neural Networks","created":"2023-07-29","cited_by_count":15,"area":null},{"id":"2401.09125","title":"Understanding Heterophily for Graph Neural Networks","created":"2024-01-17","cited_by_count":15,"area":null},{"id":"2308.09517","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","created":"2023-08-18","cited_by_count":14,"area":null},{"id":"2205.13700","title":"ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting","created":"2022-05-27","cited_by_count":13,"area":null},{"id":"2307.16026","title":"MUSE: Multi-View Contrastive Learning for Heterophilic Graphs","created":"2023-07-29","cited_by_count":13,"area":null},{"id":"2401.09769","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","created":"2024-01-18","cited_by_count":13,"area":null},{"id":"2206.03601","title":"Decoupled Self-supervised Learning for Non-Homophilous Graphs","created":"2022-06-07","cited_by_count":10,"area":null},{"id":"2305.09958","title":"SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation","created":"2023-05-17","cited_by_count":10,"area":null},{"id":"2206.02386","title":"Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering","created":"2022-06-06","cited_by_count":9,"area":null},{"id":"2312.09041","title":"Graph Neural Networks with Diverse Spectral Filtering","created":"2023-12-14","cited_by_count":9,"area":null},{"id":"2406.18854","title":"What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2301.09851","title":"Neighborhood Homophily-based Graph Convolutional Network","created":"2023-01-24","cited_by_count":7,"area":null},{"id":"2302.03228","title":"Heterophily-Aware Graph Attention Network","created":"2023-02-07","cited_by_count":7,"area":null},{"id":"2306.12139","title":"Spatial Heterophily Aware Graph Neural Networks","created":"2023-06-21","cited_by_count":7,"area":null},{"id":"2311.05185","title":"Mixture of Weak & Strong Experts on Graphs","created":"2023-11-09","cited_by_count":7,"area":null},{"id":"2311.06554","title":"PGODE: Towards High-quality System Dynamics Modeling","created":"2023-11-11","cited_by_count":7,"area":null},{"id":"2406.10871","title":"Graph Neural Reaction Diffusion Models","created":"2024-06-16","cited_by_count":6,"area":"diffusion"},{"id":"2406.12539","title":"The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs","created":"2024-06-18","cited_by_count":6,"area":null},{"id":"2407.10916","title":"When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark","created":"2024-07-15","cited_by_count":6,"area":null},{"id":"2408.09490","title":"Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts","created":"2024-08-18","cited_by_count":6,"area":null},{"id":"2409.11504","title":"Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph","created":"2024-09-17","cited_by_count":6,"area":null},{"id":"2410.02622","title":"Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms","created":"2024-10-03","cited_by_count":6,"area":null},{"id":"2305.13115","title":"Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention","created":"2023-05-22","cited_by_count":5,"area":null},{"id":"2306.02285","title":"Clarify Confused Nodes via Separated Learning","created":"2023-06-04","cited_by_count":5,"area":null},{"id":"2307.08881","title":"Examining the Effects of Degree Distribution and Homophily in Graph Learning Models","created":"2023-07-17","cited_by_count":5,"area":null},{"id":"2405.17768","title":"Revisiting the Message Passing in Heterophilous Graph Neural Networks","created":"2024-05-28","cited_by_count":5,"area":null},{"id":"2511.06608","title":"Beyond Fixed Depth: Adaptive Graph Neural Networks for Node Classification Under Varying Homophily","created":"2025-11-10","cited_by_count":5,"area":null},{"id":"2203.07678","title":"Incorporating Heterophily into Graph Neural Networks for Graph Classification","created":"2022-03-15","cited_by_count":4,"area":null},{"id":"2211.11761","title":"From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm","created":"2022-11-21","cited_by_count":4,"area":null},{"id":"2212.08966","title":"Graph Learning and Its Advancements on Large Language Models: A Holistic Survey","created":"2022-12-17","cited_by_count":4,"area":null},{"id":"2304.10896","title":"GCNH: A Simple Method For Representation Learning On Heterophilous Graphs","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2308.01650","title":"UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification","created":"2023-08-03","cited_by_count":4,"area":null},{"id":"2312.16418","title":"Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks","created":"2023-12-27","cited_by_count":4,"area":null},{"id":"2402.14393","title":"Graph Parsing Networks","created":"2024-02-22","cited_by_count":4,"area":null},{"id":"2406.10863","title":"Global-Local Graph Neural Networks for Node-Classification","created":"2024-06-16","cited_by_count":4,"area":null},{"id":"2412.00418","title":"Mixture of Experts for Node Classification","created":"2024-11-30","cited_by_count":4,"area":"moe"},{"id":"2403.03676","title":"Simplified PCNet with Robustness","created":"2024-03-06","cited_by_count":3,"area":null},{"id":"2403.10339","title":"Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection","created":"2024-03-15","cited_by_count":3,"area":null},{"id":"2403.17351","title":"Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network","created":"2024-03-26","cited_by_count":3,"area":null},{"id":"2407.18564","title":"Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2203.01564","title":"Graph Representation Learning Beyond Node and Homophily","created":"2022-03-03","cited_by_count":2,"area":null},{"id":"2211.10990","title":"Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach","created":"2022-11-20","cited_by_count":2,"area":null},{"id":"2306.08862","title":"Hyperbolic Convolution via Kernel Point Aggregation","created":"2023-06-15","cited_by_count":2,"area":null},{"id":"2403.20221","title":"Graph Neural Aggregation-diffusion with Metastability","created":"2024-03-29","cited_by_count":2,"area":null},{"id":"2405.16185","title":"Differentiable Cluster Graph Neural Network","created":"2024-05-25","cited_by_count":2,"area":null},{"id":"2407.18480","title":"Scalable Graph Compressed Convolutions","created":"2024-07-26","cited_by_count":2,"area":null},{"id":"2505.20034","title":"Graph Wave Networks","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2506.08871","title":"Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery","created":"2025-06-10","cited_by_count":2,"area":null},{"id":"2202.02470","title":"MarkovGNN: Graph Neural Networks on Markov Diffusion","created":"2022-02-05","cited_by_count":1,"area":null},{"id":"2211.16631","title":"Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2304.04391","title":"CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs","created":"2023-04-10","cited_by_count":1,"area":null},{"id":"2402.14802","title":"Link Prediction with Physics-Inspired Graph Neural Networks","created":"2024-02-22","cited_by_count":1,"area":null},{"id":"2404.00589","title":"Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing","created":"2024-03-31","cited_by_count":1,"area":null},{"id":"2412.01757","title":"Structure-Guided Input Graph for GNNs facing Heterophily","created":"2024-12-02","cited_by_count":1,"area":null},{"id":"2504.21206","title":"FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2510.22289","title":"Does Homophily Help in Robust Test-time Node Classification?","created":"2025-10-25","cited_by_count":1,"area":null},{"id":"2605.08679","title":"Attention-based graph neural networks: a survey","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2306.12943","title":"Evolving Computation Graphs","created":"2023-06-22","cited_by_count":0,"area":null},{"id":"2311.18177","title":"An Effective Universal Polynomial Basis for Spectral Graph Neural Networks","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2405.20640","title":"Heterophilous Distribution Propagation for Graph Neural Networks","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2407.19941","title":"Boosting Graph Foundation Model from Structural Perspective","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2410.02158","title":"SCNode: Spatial and Contextual Coordinates for Graph Representation Learning","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2410.03901","title":"Improving Node Representation by Boosting Target-Aware Contrastive Loss","created":"2024-10-04","cited_by_count":0,"area":null},{"id":"2504.11699","title":"H$^3$GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning","created":"2025-04-16","cited_by_count":0,"area":"distill"},{"id":"2505.22362","title":"Directed Homophily-Aware Graph Neural Network","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2508.17531","title":"Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax","created":"2025-08-24","cited_by_count":0,"area":null},{"id":"2509.13139","title":"Learning from Heterophilic Graphs: A Spectral Theory Perspective on the Impact of Self-Loops and Parallel Edges","created":"2025-09-16","cited_by_count":0,"area":null},{"id":"2509.15024","title":"Attention Beyond Neighborhoods: Reviving Transformer for Graph Clustering","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2509.19084","title":"Bridging Computational Social Science and Deep Learning: Cultural Dissemination-Inspired Graph Neural Networks","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2603.09195","title":"$P^2$GNN: Two Prototype Sets to boost GNN Performance","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2605.21247","title":"Graph Navier Stokes Networks","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2609.06521","title":"Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.37972","title":"Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}