{"total":178,"rows":[{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167,"area":null},{"id":"2205.10803","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","created":"2022-05-22","cited_by_count":448,"area":null},{"id":"2103.00111","title":"Graph Self-Supervised Learning: A Survey","created":"2021-02-27","cited_by_count":289,"area":null},{"id":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184,"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":"2304.04779","title":"GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner","created":"2023-04-10","cited_by_count":132,"area":null},{"id":"2411.06070","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","created":"2024-11-09","cited_by_count":85,"area":null},{"id":"2210.02997","title":"Expander Graph Propagation","created":"2022-10-06","cited_by_count":75,"area":null},{"id":"2206.08332","title":"BYOL-Explore: Exploration by Bootstrapped Prediction","created":"2022-06-16","cited_by_count":73,"area":null},{"id":"2202.08871","title":"Graph Data Augmentation for Graph Machine Learning: A Survey","created":"2022-02-17","cited_by_count":62,"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":"2205.07865","title":"Simple Contrastive Graph Clustering","created":"2022-05-11","cited_by_count":56,"area":null},{"id":"2310.14753","title":"Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules","created":"2023-10-23","cited_by_count":49,"area":null},{"id":"2208.09957","title":"Heterogeneous Graph Masked Autoencoders","created":"2022-08-21","cited_by_count":47,"area":null},{"id":"2406.10727","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","created":"2024-06-15","cited_by_count":44,"area":null},{"id":"2211.10738","title":"Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure","created":"2022-11-19","cited_by_count":43,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2404.14999","title":"A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data","created":"2024-04-23","cited_by_count":39,"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":"2404.11568","title":"On the Scalability of GNNs for Molecular Graphs","created":"2024-04-17","cited_by_count":36,"area":null},{"id":"2210.02016","title":"Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization","created":"2022-10-05","cited_by_count":35,"area":null},{"id":"2412.16441","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","created":"2024-12-21","cited_by_count":32,"area":null},{"id":"2405.11868","title":"Towards Graph Contrastive Learning: A Survey and Beyond","created":"2024-05-20","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":"2301.12458","title":"SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking","created":"2023-01-29","cited_by_count":23,"area":null},{"id":"2309.16014","title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","created":"2023-09-27","cited_by_count":22,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2202.02989","title":"Graph Self-supervised Learning with Accurate Discrepancy Learning","created":"2022-02-07","cited_by_count":21,"area":null},{"id":"2205.11332","title":"ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification","created":"2022-05-23","cited_by_count":21,"area":null},{"id":"2206.02796","title":"Mixed Graph Contrastive Network for Semi-Supervised Node Classification","created":"2022-06-06","cited_by_count":21,"area":null},{"id":"2206.04739","title":"I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs","created":"2022-06-09","cited_by_count":21,"area":null},{"id":"2308.09663","title":"GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction","created":"2023-08-18","cited_by_count":21,"area":null},{"id":"2410.10365","title":"SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples","created":"2024-10-14","cited_by_count":20,"area":null},{"id":"2203.09034","title":"GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis","created":"2022-03-17","cited_by_count":19,"area":null},{"id":"2410.13798","title":"Learning Graph Quantized Tokenizers","created":"2024-10-17","cited_by_count":19,"area":null},{"id":"2306.09614","title":"HomoGCL: Rethinking Homophily in Graph Contrastive Learning","created":"2023-06-16","cited_by_count":18,"area":null},{"id":"2411.11098","title":"MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild","created":"2024-11-17","cited_by_count":17,"area":null},{"id":"2209.13964","title":"Graph Soft-Contrastive Learning via Neighborhood Ranking","created":"2022-09-28","cited_by_count":16,"area":null},{"id":"2310.18777","title":"Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings","created":"2023-10-28","cited_by_count":15,"area":null},{"id":"2402.17238","title":"Does Negative Sampling Matter? A Review with Insights into its Theory and Applications","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2406.02953","title":"GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment","created":"2024-06-05","cited_by_count":15,"area":null},{"id":"2505.19024","title":"Learn Beneficial Noise as Graph Augmentation","created":"2025-05-25","cited_by_count":15,"area":null},{"id":"2210.00248","title":"Heterogeneous Graph Contrastive Multi-view Learning","created":"2022-10-01","cited_by_count":14,"area":null},{"id":"2210.08792","title":"Unifying Graph Contrastive Learning with Flexible Contextual Scopes","created":"2022-10-17","cited_by_count":14,"area":null},{"id":"2412.21151","title":"PyG-SSL: A Graph Self-Supervised Learning Toolkit","created":"2024-12-30","cited_by_count":14,"area":null},{"id":"2206.12933","title":"Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning","created":"2022-06-26","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":"2308.09198","title":"Half-Hop: A graph upsampling approach for slowing down message passing","created":"2023-08-17","cited_by_count":13,"area":null},{"id":"2302.05428","title":"STERLING: Synergistic Representation Learning on Bipartite Graphs","created":"2023-01-25","cited_by_count":12,"area":null},{"id":"2408.03765","title":"Reliable Node Similarity Matrix Guided Contrastive Graph Clustering","created":"2024-08-07","cited_by_count":12,"area":null},{"id":"2208.10493","title":"Relational Self-Supervised Learning on Graphs","created":"2022-08-21","cited_by_count":11,"area":null},{"id":"2211.14394","title":"Link Prediction with Non-Contrastive Learning","created":"2022-11-25","cited_by_count":11,"area":null},{"id":"2505.16130","title":"Generative Graph Pattern Machine","created":"2025-05-22","cited_by_count":11,"area":null},{"id":"2204.01303","title":"GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment","created":"2022-04-04","cited_by_count":10,"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":"2212.09970","title":"Data Augmentation on Graphs: A Technical Survey","created":"2022-12-20","cited_by_count":10,"area":null},{"id":"2304.01507","title":"RARE: Robust Masked Graph Autoencoder","created":"2023-04-04","cited_by_count":10,"area":null},{"id":"2306.08076","title":"Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization","created":"2023-06-13","cited_by_count":10,"area":null},{"id":"2312.04142","title":"TimeDRL: Disentangled Representation Learning for Multivariate Time-Series","created":"2023-12-07","cited_by_count":10,"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":"2307.03591","title":"Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning","created":"2023-07-06","cited_by_count":8,"area":null},{"id":"2208.02810","title":"Analyzing Data-Centric Properties for Graph Contrastive Learning","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2303.12341","title":"EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning","created":"2023-03-22","cited_by_count":7,"area":null},{"id":"2311.02687","title":"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning","created":"2023-11-05","cited_by_count":7,"area":null},{"id":"2306.06936","title":"CARL-G: Clustering-Accelerated Representation Learning on Graphs","created":"2023-06-12","cited_by_count":6,"area":null},{"id":"2306.13854","title":"Similarity Preserving Adversarial Graph Contrastive Learning","created":"2023-06-24","cited_by_count":6,"area":null},{"id":"2307.15244","title":"BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection","created":"2023-07-28","cited_by_count":6,"area":null},{"id":"2309.13599","title":"From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited","created":"2023-09-24","cited_by_count":6,"area":null},{"id":"2406.05482","title":"Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks","created":"2024-06-08","cited_by_count":6,"area":null},{"id":"2408.05087","title":"Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning","created":"2024-08-09","cited_by_count":6,"area":null},{"id":"2112.12482","title":"Self-Supervised Graph Representation Learning for Neuronal Morphologies","created":"2021-12-23","cited_by_count":5,"area":null},{"id":"2210.16906","title":"DyG2Vec: Efficient Representation Learning for Dynamic Graphs","created":"2022-10-30","cited_by_count":5,"area":null},{"id":"2303.06344","title":"Graph Contrastive Learning under Heterophily via Graph Filters","created":"2023-03-11","cited_by_count":5,"area":null},{"id":"2310.03312","title":"Certifiably Robust Graph Contrastive Learning","created":"2023-10-05","cited_by_count":5,"area":null},{"id":"2401.12681","title":"Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning","created":"2024-01-23","cited_by_count":5,"area":null},{"id":"2411.01157","title":"Negative-Free Self-Supervised Gaussian Embedding of Graphs","created":"2024-11-02","cited_by_count":5,"area":null},{"id":"2505.05533","title":"Rethinking Graph Contrastive Learning through Relative Similarity Preservation","created":"2025-05-08","cited_by_count":5,"area":null},{"id":"2206.07041","title":"Learning Behavior Representations Through Multi-Timescale Bootstrapping","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2207.01792","title":"Features Based Adaptive Augmentation for Graph Contrastive Learning","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2210.00162","title":"Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning","created":"2022-10-01","cited_by_count":4,"area":null},{"id":"2312.16830","title":"A Contrastive Variational Graph Auto-Encoder for Node Clustering","created":"2023-12-28","cited_by_count":4,"area":null},{"id":"2406.08310","title":"GraphFM: A Comprehensive Benchmark for Graph Foundation Model","created":"2024-06-12","cited_by_count":4,"area":null},{"id":"2406.15044","title":"From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning","created":"2024-06-21","cited_by_count":4,"area":null},{"id":"2408.11243","title":"Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?","created":"2024-08-20","cited_by_count":4,"area":null},{"id":"2409.01145","title":"LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning","created":"2024-09-02","cited_by_count":4,"area":null},{"id":"2502.13277","title":"HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views","created":"2025-02-18","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},{"id":"2402.09586","title":"WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization","created":"2024-02-14","cited_by_count":3,"area":null},{"id":"2402.15680","title":"Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks","created":"2024-02-24","cited_by_count":3,"area":null},{"id":"2506.08326","title":"Graph Prompting for Graph Learning Models: Recent Advances and Future Directions","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2205.00905","title":"FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation","created":"2022-05-02","cited_by_count":2,"area":null},{"id":"2302.04626","title":"Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment","created":"2023-02-09","cited_by_count":2,"area":null},{"id":"2402.08023","title":"UGMAE: A Unified Framework for Graph Masked Autoencoders","created":"2024-02-12","cited_by_count":2,"area":null},{"id":"2407.21236","title":"GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks","created":"2024-07-30","cited_by_count":2,"area":null},{"id":"2410.12606","title":"Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2506.14098","title":"Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks","created":"2025-06-17","cited_by_count":2,"area":null},{"id":"2507.13368","title":"Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2508.03104","title":"HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation","created":"2025-08-05","cited_by_count":2,"area":null},{"id":"2508.06588","title":"Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning","created":"2025-08-08","cited_by_count":2,"area":null}],"next_cursor":"100"}