{"total":89,"rows":[{"id":"2302.08043","title":"GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks","created":"2023-02-16","cited_by_count":167,"area":null},{"id":"2110.03753","title":"From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness","created":"2021-10-07","cited_by_count":155,"area":null},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137,"area":null},{"id":"2110.15438","title":"InfoGCL: Information-Aware Graph Contrastive Learning","created":"2021-10-28","cited_by_count":105,"area":null},{"id":"2305.12600","title":"PRODIGY: Enabling In-context Learning Over Graphs","created":"2023-05-21","cited_by_count":98,"area":null},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80,"area":null},{"id":"2306.06063","title":"Virtual Node Tuning for Few-shot Node Classification","created":"2023-06-09","cited_by_count":46,"area":null},{"id":"2206.11972","title":"Task-Adaptive Few-shot Node Classification","created":"2022-06-23","cited_by_count":43,"area":null},{"id":"2311.15317","title":"Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs","created":"2023-11-26","cited_by_count":39,"area":null},{"id":"2202.13538","title":"Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning","created":"2022-02-28","cited_by_count":28,"area":null},{"id":"2210.12130","title":"Graph Few-shot Learning with Task-specific Structures","created":"2022-10-21","cited_by_count":26,"area":null},{"id":"2212.05606","title":"Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification","created":"2022-12-11","cited_by_count":24,"area":null},{"id":"2310.09872","title":"Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs","created":"2023-10-15","cited_by_count":23,"area":null},{"id":"2205.02435","title":"FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs","created":"2022-05-05","cited_by_count":22,"area":null},{"id":"2205.13954","title":"Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation","created":"2022-05-27","cited_by_count":22,"area":"distill"},{"id":"2210.08654","title":"Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs","created":"2022-10-16","cited_by_count":21,"area":null},{"id":"2201.07951","title":"Informative Pseudo-Labeling for Graph Neural Networks with Few Labels","created":"2022-01-20","cited_by_count":19,"area":null},{"id":"2201.08265","title":"Cross-Domain Few-Shot Graph Classification","created":"2022-01-20","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":"2308.13821","title":"A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions","created":"2023-08-26","cited_by_count":15,"area":null},{"id":"2212.01793","title":"kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning","created":"2022-12-04","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":"2505.16225","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","created":"2025-05-22","cited_by_count":13,"area":null},{"id":"2203.09308","title":"Few-Shot Learning on Graphs","created":"2022-03-17","cited_by_count":11,"area":null},{"id":"2208.12422","title":"Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity","created":"2022-08-26","cited_by_count":11,"area":null},{"id":"2306.15154","title":"Contrastive Meta-Learning for Few-shot Node Classification","created":"2023-06-27","cited_by_count":11,"area":null},{"id":"2407.15431","title":"Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs","created":"2024-07-22","cited_by_count":11,"area":null},{"id":"2103.10334","title":"Structure Inducing Pre-Training","created":"2021-03-18","cited_by_count":10,"area":null},{"id":"2107.10609","title":"Data Considerations in Graph Representation Learning for Supply Chain Networks","created":"2021-07-22","cited_by_count":10,"area":null},{"id":"2203.15936","title":"Supervised Graph Contrastive Learning for Few-shot Node Classification","created":"2022-03-29","cited_by_count":9,"area":null},{"id":"2406.01943","title":"Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs","created":"2024-06-04","cited_by_count":9,"area":null},{"id":"2202.00097","title":"Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data","created":"2022-01-31","cited_by_count":8,"area":null},{"id":"2303.03379","title":"SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning","created":"2023-03-06","cited_by_count":8,"area":null},{"id":"2305.18758","title":"Task-Equivariant Graph Few-shot Learning","created":"2023-05-30","cited_by_count":8,"area":null},{"id":"2408.05920","title":"Urban Region Pre-training and Prompting: A Graph-based Approach","created":"2024-08-12","cited_by_count":8,"area":null},{"id":"2103.00137","title":"Meta-Learning with Graph Neural Networks: Methods and Applications","created":"2021-02-27","cited_by_count":7,"area":null},{"id":"2110.13798","title":"Deeper-GXX: Deepening Arbitrary GNNs","created":"2021-10-26","cited_by_count":7,"area":null},{"id":"2210.07309","title":"SHINE: SubHypergraph Inductive Neural nEtwork","created":"2022-10-13","cited_by_count":7,"area":null},{"id":"2303.13763","title":"Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs","created":"2023-03-24","cited_by_count":7,"area":"distill"},{"id":"2407.14732","title":"Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training","created":"2024-07-20","cited_by_count":7,"area":null},{"id":"2103.00771","title":"Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning","created":"2021-03-01","cited_by_count":6,"area":null},{"id":"2306.01631","title":"Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations","created":"2023-06-02","cited_by_count":6,"area":null},{"id":"2401.03597","title":"Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs","created":"2024-01-07","cited_by_count":6,"area":null},{"id":"2402.01440","title":"A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning","created":"2024-02-02","cited_by_count":6,"area":null},{"id":"2501.05635","title":"Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport","created":"2025-01-10","cited_by_count":6,"area":null},{"id":"2310.18918","title":"Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach","created":"2023-10-29","cited_by_count":5,"area":null},{"id":"2404.12569","title":"Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data","created":"2024-04-19","cited_by_count":5,"area":null},{"id":"2502.14158","title":"Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks","created":"2025-02-19","cited_by_count":5,"area":null},{"id":"2207.05068","title":"Few-Shot Semantic Relation Prediction across Heterogeneous Graphs","created":"2022-07-11","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":"2305.15745","title":"Robust Ante-hoc Graph Explainer using Bilevel Optimization","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2411.12913","title":"MLDGG: Meta-Learning for Domain Generalization on Graphs","created":"2024-11-19","cited_by_count":4,"area":null},{"id":"2604.08553","title":"GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback","created":"2026-03-11","cited_by_count":4,"area":null},{"id":"2306.08192","title":"Inductive Linear Probing for Few-shot Node Classification","created":"2023-06-14","cited_by_count":3,"area":null},{"id":"2308.05275","title":"Cross-heterogeneity Graph Few-shot Learning","created":"2023-08-10","cited_by_count":3,"area":null},{"id":"2309.10376","title":"Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks","created":"2023-09-19","cited_by_count":3,"area":null},{"id":"2407.13989","title":"Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models","created":"2024-07-19","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":"2510.12140","title":"Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2106.06873","title":"Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification","created":"2021-06-12","cited_by_count":2,"area":null},{"id":"2209.00508","title":"Models and Benchmarks for Representation Learning of Partially Observed Subgraphs","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2209.13410","title":"Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression","created":"2022-09-24","cited_by_count":2,"area":null},{"id":"2308.14338","title":"Fair Few-shot Learning with Auxiliary Sets","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2403.14340","title":"Exploring Task Unification in Graph Representation Learning via Generative Approach","created":"2024-03-21","cited_by_count":2,"area":null},{"id":"2502.07968","title":"Generative Risk Minimization for Out-of-Distribution Generalization on Graphs","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2507.07621","title":"Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation","created":"2025-07-10","cited_by_count":2,"area":null},{"id":"2210.00084","title":"Contrastive Graph Few-Shot Learning","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2301.04824","title":"A Network Science perspective of Graph Convolutional Networks: A survey","created":"2023-01-12","cited_by_count":1,"area":null},{"id":"2306.15217","title":"Unsupervised Episode Generation for Graph Meta-learning","created":"2023-06-27","cited_by_count":1,"area":null},{"id":"2310.09787","title":"Dynamic Link Prediction for New Nodes in Temporal Graph Networks","created":"2023-10-15","cited_by_count":1,"area":null},{"id":"2402.08979","title":"Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing","created":"2024-02-14","cited_by_count":1,"area":null},{"id":"2404.01822","title":"A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection","created":"2024-04-02","cited_by_count":1,"area":null},{"id":"2406.04639","title":"Cooperative Meta-Learning with Gradient Augmentation","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2406.04825","title":"Graph Mining under Data scarcity","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2408.00525","title":"Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion","created":"2024-08-01","cited_by_count":1,"area":null},{"id":"2408.14520","title":"Towards Graph Prompt Learning: A Survey and Beyond","created":"2024-08-26","cited_by_count":1,"area":null},{"id":"2605.24410","title":"Advancing Graph Few-Shot Learning via In-Context Learning","created":"2026-05-23","cited_by_count":1,"area":null},{"id":"2302.13551","title":"Invariant Layers for Graphs with Nodes of Different Types","created":"2023-02-27","cited_by_count":0,"area":null},{"id":"2402.00450","title":"CPT: Competence-progressive Training Strategy for Few-shot Node Classification","created":"2024-02-01","cited_by_count":0,"area":null},{"id":"2503.16755","title":"Fast online node labeling with graph subsampling","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2504.04963","title":"Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2504.13691","title":"MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2604.00399","title":"A Cross-graph Tuning-free GNN Prompting Framework","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.06391","title":"Toward a universal foundation model for graph-structured data","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.17271","title":"HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2604.22031","title":"Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2604.27462","title":"Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion","created":"2026-04-30","cited_by_count":0,"area":"diffusion"},{"id":"2606.11583","title":"Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2609.23547","title":"Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport","created":"2026-09-20","cited_by_count":0,"area":null}],"next_cursor":null}