{"total":168,"rows":[{"id":"2306.08385","title":"NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification","created":"2023-06-14","cited_by_count":210,"area":null},{"id":"2106.05819","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","created":"2021-06-10","cited_by_count":200,"area":null},{"id":"2201.12987","title":"Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism","created":"2022-01-31","cited_by_count":171,"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":"2110.15438","title":"InfoGCL: Information-Aware Graph Contrastive Learning","created":"2021-10-28","cited_by_count":105,"area":null},{"id":"2110.14038","title":"Robustness of Graph Neural Networks at Scale","created":"2021-10-26","cited_by_count":100,"area":null},{"id":"2110.09443","title":"Beltrami Flow and Neural Diffusion on Graphs","created":"2021-10-18","cited_by_count":84,"area":null},{"id":"2112.08903","title":"Graph Structure Learning with Variational Information Bottleneck","created":"2021-12-16","cited_by_count":81,"area":null},{"id":"2107.01495","title":"On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs","created":"2021-07-03","cited_by_count":49,"area":null},{"id":"2112.09899","title":"Improving Subgraph Recognition with Variational Graph Information Bottleneck","created":"2021-12-18","cited_by_count":45,"area":null},{"id":"2310.16520","title":"Towards Self-Interpretable Graph-Level Anomaly Detection","created":"2023-10-25","cited_by_count":41,"area":null},{"id":"2301.13694","title":"Are Defenses for Graph Neural Networks Robust?","created":"2023-01-31","cited_by_count":38,"area":null},{"id":"2306.07651","title":"Variational Positive-incentive Noise: How Noise Benefits Models","created":"2023-06-13","cited_by_count":38,"area":null},{"id":"2505.19234","title":"GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling","created":"2025-05-25","cited_by_count":36,"area":"agents"},{"id":"2303.09778","title":"SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization","created":"2023-03-17","cited_by_count":35,"area":null},{"id":"2405.09308","title":"TimeX++: Learning Time-Series Explanations with Information Bottleneck","created":"2024-05-15","cited_by_count":33,"area":null},{"id":"2202.10720","title":"EIGNN: Efficient Infinite-Depth Graph Neural Networks","created":"2022-02-22","cited_by_count":32,"area":null},{"id":"2204.10037","title":"DropMessage: Unifying Random Dropping for Graph Neural Networks","created":"2022-04-21","cited_by_count":29,"area":null},{"id":"2306.01103","title":"Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization","created":"2023-06-01","cited_by_count":29,"area":null},{"id":"2103.11155","title":"Recognizing Predictive Substructures with Subgraph Information Bottleneck","created":"2021-03-20","cited_by_count":28,"area":null},{"id":"2109.14509","title":"PAC-Bayes Information Bottleneck","created":"2021-09-29","cited_by_count":28,"area":null},{"id":"2201.01702","title":"Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations","created":"2022-01-04","cited_by_count":28,"area":null},{"id":"2210.16966","title":"Interpretable Geometric Deep Learning via Learnable Randomness Injection","created":"2022-10-30","cited_by_count":27,"area":null},{"id":"2305.01520","title":"Conditional Graph Information Bottleneck for Molecular Relational Learning","created":"2023-04-29","cited_by_count":24,"area":null},{"id":"2312.02545","title":"Graph Information Bottleneck for Remote Sensing Segmentation","created":"2023-12-05","cited_by_count":24,"area":null},{"id":"2310.19906","title":"Interpretable Prototype-based Graph Information Bottleneck","created":"2023-10-30","cited_by_count":23,"area":null},{"id":"2105.04854","title":"Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity","created":"2021-05-11","cited_by_count":20,"area":null},{"id":"2201.05540","title":"Compact Graph Structure Learning via Mutual Information Compression","created":"2022-01-14","cited_by_count":20,"area":null},{"id":"2211.06545","title":"Self-Supervised Graph Structure Refinement for Graph Neural Networks","created":"2022-11-12","cited_by_count":20,"area":null},{"id":"2402.05962","title":"EXGC: Bridging Efficiency and Explainability in Graph Condensation","created":"2024-02-05","cited_by_count":20,"area":null},{"id":"2310.00841","title":"Drug Discovery with Dynamic Goal-aware Fragments","created":"2023-10-02","cited_by_count":19,"area":null},{"id":"2307.07832","title":"MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation","created":"2023-07-15","cited_by_count":18,"area":null},{"id":"2405.17898","title":"FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction","created":"2024-05-28","cited_by_count":18,"area":null},{"id":"2205.03612","title":"BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck","created":"2022-05-07","cited_by_count":17,"area":null},{"id":"2401.06176","title":"GOODAT: Towards Test-time Graph Out-of-Distribution Detection","created":"2024-01-10","cited_by_count":17,"area":null},{"id":"2410.19265","title":"A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation","created":"2024-10-25","cited_by_count":17,"area":null},{"id":"2111.01705","title":"AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements","created":"2021-11-02","cited_by_count":16,"area":null},{"id":"2305.11719","title":"Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling","created":"2023-05-19","cited_by_count":16,"area":null},{"id":"2306.09104","title":"On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation","created":"2023-06-15","cited_by_count":16,"area":null},{"id":"2405.11225","title":"SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection","created":"2024-05-18","cited_by_count":16,"area":null},{"id":"2406.01549","title":"An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation","created":"2024-06-03","cited_by_count":16,"area":"rag"},{"id":"2204.12496","title":"Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering","created":"2022-04-26","cited_by_count":15,"area":null},{"id":"2306.15902","title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","created":"2023-06-28","cited_by_count":15,"area":null},{"id":"2308.05081","title":"Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling","created":"2023-08-09","cited_by_count":15,"area":null},{"id":"2208.09126","title":"GraphTTA: Test Time Adaptation on Graph Neural Networks","created":"2022-08-19","cited_by_count":14,"area":null},{"id":"2301.02708","title":"Few-shot Node Classification with Extremely Weak Supervision","created":"2023-01-06","cited_by_count":14,"area":null},{"id":"2306.08604","title":"A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy","created":"2023-06-14","cited_by_count":14,"area":null},{"id":"2312.12340","title":"Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers","created":"2023-12-19","cited_by_count":14,"area":null},{"id":"2105.11866","title":"GraphFM: Graph Factorization Machines for Feature Interaction Modeling","created":"2021-05-25","cited_by_count":13,"area":null},{"id":"2310.17149","title":"Explainable Spatio-Temporal Graph Neural Networks","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2312.08616","title":"A Generalized Neural Diffusion Framework on Graphs","created":"2023-12-14","cited_by_count":13,"area":null},{"id":"2406.07955","title":"How Interpretable Are Interpretable Graph Neural Networks?","created":"2024-06-12","cited_by_count":13,"area":null},{"id":"2306.11264","title":"GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks","created":"2023-06-20","cited_by_count":12,"area":null},{"id":"2310.10191","title":"VIBE: Topic-Driven Temporal Adaptation for Twitter Classification","created":"2023-10-16","cited_by_count":12,"area":null},{"id":"2209.07086","title":"Earthquake Phase Association with Graph Neural Networks","created":"2022-09-15","cited_by_count":11,"area":null},{"id":"2502.02719","title":"Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective","created":"2025-02-04","cited_by_count":11,"area":null},{"id":"2105.12419","title":"Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge","created":"2021-05-26","cited_by_count":10,"area":null},{"id":"2312.02203","title":"Learning High-Order Relationships of Brain Regions","created":"2023-12-02","cited_by_count":10,"area":null},{"id":"2108.06530","title":"Neuron Campaign for Initialization Guided by Information Bottleneck Theory","created":"2021-08-14","cited_by_count":9,"area":null},{"id":"2206.00118","title":"Principle of Relevant Information for Graph Sparsification","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2206.09345","title":"Finding Diverse and Predictable Subgraphs for Graph Domain Generalization","created":"2022-06-19","cited_by_count":9,"area":null},{"id":"2307.07840","title":"RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks","created":"2023-07-15","cited_by_count":9,"area":null},{"id":"2311.01196","title":"Combating Bilateral Edge Noise for Robust Link Prediction","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2403.16137","title":"A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective","created":"2024-03-24","cited_by_count":9,"area":null},{"id":"2406.13214","title":"Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck","created":"2024-06-19","cited_by_count":9,"area":null},{"id":"2212.08487","title":"Semantics-Empowered Communication: A Tutorial-cum-Survey","created":"2022-12-16","cited_by_count":8,"area":null},{"id":"2312.05596","title":"Factorized Explainer for Graph Neural Networks","created":"2023-12-09","cited_by_count":8,"area":null},{"id":"2405.03911","title":"Federated Graph Condensation with Information Bottleneck Principles","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2407.01979","title":"Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks","created":"2024-07-02","cited_by_count":8,"area":null},{"id":"2505.16860","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","created":"2025-05-22","cited_by_count":8,"area":null},{"id":"2507.14677","title":"Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective","created":"2025-07-19","cited_by_count":8,"area":null},{"id":"2602.12532","title":"CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning","created":"2026-02-13","cited_by_count":8,"area":"vla"},{"id":"2203.10866","title":"Unsupervised Network Embedding Beyond Homophily","created":"2022-03-21","cited_by_count":7,"area":null},{"id":"2205.07426","title":"Optimal Randomized Approximations for Matrix based Renyi's Entropy","created":"2022-05-16","cited_by_count":7,"area":null},{"id":"2208.12547","title":"Deep Hypergraph Structure Learning","created":"2022-08-26","cited_by_count":7,"area":null},{"id":"2307.13055","title":"MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning","created":"2023-07-24","cited_by_count":7,"area":null},{"id":"2502.01688","title":"BrainOOD: Out-of-distribution Generalizable Brain Network Analysis","created":"2025-02-02","cited_by_count":7,"area":null},{"id":"2602.04116","title":"Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach","created":"2026-02-04","cited_by_count":7,"area":null},{"id":"2607.21973","title":"Rethinking Layer-Wise Information Allocation for Vision Foundation Model Adaptation","created":"2026-07-24","cited_by_count":7,"area":null},{"id":"2104.13012","title":"Structure-Aware Hierarchical Graph Pooling using Information Bottleneck","created":"2021-04-27","cited_by_count":6,"area":null},{"id":"2305.04501","title":"SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning","created":"2023-05-08","cited_by_count":6,"area":null},{"id":"2306.07699","title":"Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs","created":"2023-06-13","cited_by_count":6,"area":null},{"id":"2402.06716","title":"Dynamic Graph Information Bottleneck","created":"2024-02-09","cited_by_count":6,"area":null},{"id":"2403.01942","title":"Mitigating Label Noise on Graph via Topological Sample Selection","created":"2024-03-04","cited_by_count":6,"area":null},{"id":"2502.18331","title":"BottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle","created":"2025-02-22","cited_by_count":6,"area":null},{"id":"2402.04296","title":"LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\\times$ Faster Inference","created":"2024-02-06","cited_by_count":5,"area":null},{"id":"2502.13555","title":"Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs","created":"2025-02-19","cited_by_count":5,"area":null},{"id":"2505.13946","title":"Visual Instruction Bottleneck Tuning","created":"2025-05-20","cited_by_count":5,"area":"mllm"},{"id":"2508.12244","title":"DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning","created":"2025-08-17","cited_by_count":5,"area":null},{"id":"2106.03393","title":"Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs","created":"2021-06-07","cited_by_count":4,"area":null},{"id":"2112.13720","title":"Computationally Efficient Approximations for Matrix-based Renyi's Entropy","created":"2021-12-27","cited_by_count":4,"area":null},{"id":"2201.13025","title":"Learning Robust Representation through Graph Adversarial Contrastive Learning","created":"2022-01-31","cited_by_count":4,"area":null},{"id":"2303.06836","title":"Label Information Bottleneck for Label Enhancement","created":"2023-03-13","cited_by_count":4,"area":null},{"id":"2307.13944","title":"Entropy Neural Estimation for Graph Contrastive Learning","created":"2023-07-26","cited_by_count":4,"area":null},{"id":"2403.16656","title":"Graph Augmentation for Recommendation","created":"2024-03-25","cited_by_count":4,"area":null},{"id":"2412.17355","title":"Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck","created":"2024-12-23","cited_by_count":4,"area":null},{"id":"2412.19993","title":"Discrete Curvature Graph Information Bottleneck","created":"2024-12-28","cited_by_count":4,"area":null},{"id":"2502.13991","title":"Learning to Discover Regulatory Elements for Gene Expression Prediction","created":"2025-02-19","cited_by_count":4,"area":null},{"id":"2505.22252","title":"B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2308.01063","title":"Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach","created":"2023-08-02","cited_by_count":3,"area":null}],"next_cursor":"100"}