{"total":84,"rows":[{"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":"2304.04779","title":"GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner","created":"2023-04-10","cited_by_count":132,"area":null},{"id":"2201.06367","title":"Towards Unsupervised Deep Graph Structure Learning","created":"2022-01-17","cited_by_count":114,"area":null},{"id":"2204.04879","title":"How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision","created":"2022-04-11","cited_by_count":102,"area":null},{"id":"2205.10053","title":"What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders","created":"2022-05-20","cited_by_count":82,"area":null},{"id":"2206.07579","title":"A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions","created":"2022-06-15","cited_by_count":73,"area":null},{"id":"2203.15737","title":"Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version","created":"2022-03-29","cited_by_count":59,"area":null},{"id":"2104.07886","title":"Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks","created":"2021-04-16","cited_by_count":38,"area":null},{"id":"2105.03170","title":"FedGL: Federated Graph Learning Framework with Global Self-Supervision","created":"2021-05-07","cited_by_count":34,"area":null},{"id":"2211.06614","title":"Robust Training of Graph Neural Networks via Noise Governance","created":"2022-11-12","cited_by_count":29,"area":null},{"id":"2203.15935","title":"Graph Neural Networks in IoT: A Survey","created":"2022-03-29","cited_by_count":28,"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":"2107.09928","title":"Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction","created":"2021-07-21","cited_by_count":20,"area":null},{"id":"2107.11789","title":"ROD: Reception-aware Online Distillation for Sparse Graphs","created":"2021-07-25","cited_by_count":18,"area":null},{"id":"2301.12951","title":"Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks","created":"2023-01-30","cited_by_count":16,"area":null},{"id":"2308.06827","title":"Reinforcement Graph Clustering with Unknown Cluster Number","created":"2023-08-13","cited_by_count":16,"area":null},{"id":"2103.16046","title":"Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders","created":"2021-03-30","cited_by_count":15,"area":null},{"id":"2212.05532","title":"Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges","created":"2022-12-11","cited_by_count":15,"area":null},{"id":"2301.05579","title":"A survey and taxonomy of loss functions in machine learning","created":"2023-01-13","cited_by_count":15,"area":null},{"id":"2201.09830","title":"Learning Graph Augmentations to Learn Graph Representations","created":"2022-01-24","cited_by_count":14,"area":null},{"id":"2206.00006","title":"COIN: Co-Cluster Infomax for Bipartite Graphs","created":"2022-05-31","cited_by_count":14,"area":null},{"id":"2104.04909","title":"Edge: Enriching Knowledge Graph Embeddings with External Text","created":"2021-04-11","cited_by_count":13,"area":null},{"id":"2204.12656","title":"SCGC : Self-Supervised Contrastive Graph Clustering","created":"2022-04-27","cited_by_count":13,"area":null},{"id":"2103.04683","title":"Learning Graph Neural Networks with Positive and Unlabeled Nodes","created":"2021-03-08","cited_by_count":11,"area":null},{"id":"2402.03814","title":"Masked Graph Autoencoder with Non-discrete Bandwidths","created":"2024-02-06","cited_by_count":11,"area":null},{"id":"2407.19944","title":"Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation","created":"2024-07-29","cited_by_count":11,"area":null},{"id":"2107.11576","title":"X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering","created":"2021-07-24","cited_by_count":10,"area":null},{"id":"2104.08438","title":"Bayesian graph convolutional neural networks via tempered MCMC","created":"2021-04-17","cited_by_count":9,"area":null},{"id":"2310.15523","title":"Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning","created":"2023-10-24","cited_by_count":9,"area":null},{"id":"2304.07509","title":"Multi-View Graph Representation Learning Beyond Homophily","created":"2023-04-15","cited_by_count":8,"area":null},{"id":"2105.00425","title":"Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity","created":"2021-05-02","cited_by_count":7,"area":null},{"id":"2211.03710","title":"Graph Contrastive Learning with Implicit Augmentations","created":"2022-11-07","cited_by_count":7,"area":null},{"id":"2405.04245","title":"Exploring Correlations of Self-Supervised Tasks for Graphs","created":"2024-05-07","cited_by_count":7,"area":null},{"id":"2102.02026","title":"Learning Graph Representations","created":"2021-02-03","cited_by_count":5,"area":null},{"id":"2206.07897","title":"NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering","created":"2022-06-16","cited_by_count":5,"area":null},{"id":"2103.11414","title":"Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction","created":"2021-03-21","cited_by_count":4,"area":null},{"id":"2401.11576","title":"Quantum Architecture Search with Unsupervised Representation Learning","created":"2024-01-21","cited_by_count":4,"area":null},{"id":"2503.07852","title":"CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders","created":"2025-03-10","cited_by_count":4,"area":null},{"id":"2103.04318","title":"Implementing graph neural networks with TensorFlow-Keras","created":"2021-03-07","cited_by_count":3,"area":null},{"id":"2103.04339","title":"Network Representation Learning: From Traditional Feature Learning to Deep Learning","created":"2021-03-07","cited_by_count":3,"area":null},{"id":"2107.02658","title":"On Generalization of Graph Autoencoders with Adversarial Training","created":"2021-07-06","cited_by_count":3,"area":null},{"id":"2109.00138","title":"Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks","created":"2021-09-01","cited_by_count":3,"area":null},{"id":"2110.04279","title":"StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis","created":"2021-10-06","cited_by_count":3,"area":null},{"id":"2504.12011","title":"Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition","created":"2025-04-16","cited_by_count":3,"area":null},{"id":"2505.23345","title":"Graph Positional Autoencoders as Self-supervised Learners","created":"2025-05-29","cited_by_count":3,"area":null},{"id":"2105.00695","title":"ResVGAE: Going Deeper with Residual Modules for Link Prediction","created":"2021-05-03","cited_by_count":2,"area":null},{"id":"2111.04981","title":"Wasserstein Adversarially Regularized Graph Autoencoder","created":"2021-11-09","cited_by_count":2,"area":null},{"id":"2112.15530","title":"Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning","created":"2021-12-31","cited_by_count":2,"area":null},{"id":"2203.01564","title":"Graph Representation Learning Beyond Node and Homophily","created":"2022-03-03","cited_by_count":2,"area":null},{"id":"2211.02720","title":"Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks","created":"2022-11-04","cited_by_count":2,"area":null},{"id":"2503.19070","title":"Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2506.20980","title":"Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs","created":"2025-06-26","cited_by_count":2,"area":null},{"id":"2104.13323","title":"Network Embedding via Deep Prediction Model","created":"2021-04-27","cited_by_count":1,"area":null},{"id":"2107.01936","title":"Adversarial Robustness of Probabilistic Network Embedding for Link Prediction","created":"2021-07-05","cited_by_count":1,"area":null},{"id":"2110.15742","title":"Barlow Graph Auto-Encoder for Unsupervised Network Embedding","created":"2021-10-29","cited_by_count":1,"area":null},{"id":"2111.04826","title":"Inferential SIR-GN: Scalable Graph Representation Learning","created":"2021-11-08","cited_by_count":1,"area":null},{"id":"2111.12952","title":"AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020","created":"2021-11-25","cited_by_count":1,"area":null},{"id":"2303.08958","title":"NESS: Node Embeddings from Static SubGraphs","created":"2023-03-15","cited_by_count":1,"area":null},{"id":"2311.00797","title":"Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling","created":"2023-11-01","cited_by_count":1,"area":null},{"id":"2410.10241","title":"Revisiting Graph Autoencoders as Implicit Contrastive Learners","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2511.17113","title":"AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2101.04053","title":"Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder","created":"2021-01-11","cited_by_count":0,"area":null},{"id":"2103.00067","title":"Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction","created":"2021-02-26","cited_by_count":0,"area":null},{"id":"2104.05234","title":"Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood","created":"2021-04-12","cited_by_count":0,"area":null},{"id":"2106.09241","title":"CoANE: Modeling Context Co-occurrence for Attributed Network Embedding","created":"2021-06-17","cited_by_count":0,"area":null},{"id":"2108.09136","title":"Unsupervised Domain-adaptive Hash for Networks","created":"2021-08-20","cited_by_count":0,"area":null},{"id":"2109.02358","title":"Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning","created":"2021-09-06","cited_by_count":0,"area":null},{"id":"2109.15257","title":"Latent Network Embedding via Adversarial Auto-encoders","created":"2021-09-30","cited_by_count":0,"area":null},{"id":"2201.12843","title":"Graph Representation Learning via Aggregation Enhancement","created":"2022-01-30","cited_by_count":0,"area":null},{"id":"2207.03580","title":"Dynamic Community Detection via Adversarial Temporal Graph Representation Learning","created":"2022-06-29","cited_by_count":0,"area":null},{"id":"2208.10496","title":"Representation Learning of Knowledge Graph for Wireless Communication Networks","created":"2022-08-22","cited_by_count":0,"area":null},{"id":"2209.14825","title":"Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs","created":"2022-09-29","cited_by_count":0,"area":null},{"id":"2210.12020","title":"HCL: Improving Graph Representation with Hierarchical Contrastive Learning","created":"2022-10-21","cited_by_count":0,"area":null},{"id":"2211.02396","title":"Rethinking the positive role of cluster structure in complex networks for link prediction tasks","created":"2022-11-04","cited_by_count":0,"area":null},{"id":"2211.05594","title":"A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques","created":"2022-10-13","cited_by_count":0,"area":null},{"id":"2303.00170","title":"Asymmetric Learning for Graph Neural Network based Link Prediction","created":"2023-03-01","cited_by_count":0,"area":null},{"id":"2303.15193","title":"CoCon: A Data Set on Combined Contextualized Research Artifact Use","created":"2023-03-27","cited_by_count":0,"area":null},{"id":"2309.12202","title":"Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023","created":"2023-09-14","cited_by_count":0,"area":null},{"id":"2409.15304","title":"Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks","created":"2024-09-04","cited_by_count":0,"area":null},{"id":"2502.16724","title":"To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders","created":"2025-02-23","cited_by_count":0,"area":null},{"id":"2509.21059","title":"Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2607.29115","title":"Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}