{"total":439,"rows":[{"id":"2106.07178","title":"A Comprehensive Survey on Graph Anomaly Detection with Deep Learning","created":"2021-06-14","cited_by_count":230,"area":null},{"id":"2202.04579","title":"Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs","created":"2022-02-09","cited_by_count":214,"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":"2101.05974","title":"Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks","created":"2021-01-15","cited_by_count":193,"area":null},{"id":"2105.02605","title":"GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph","created":"2021-05-06","cited_by_count":143,"area":null},{"id":"2108.03348","title":"Global Self-Attention as a Replacement for Graph Convolution","created":"2021-08-07","cited_by_count":114,"area":null},{"id":"2107.01188","title":"Combinatorial Optimization with Physics-Inspired Graph Neural Networks","created":"2021-07-02","cited_by_count":108,"area":null},{"id":"2201.07858","title":"Decoupling the Depth and Scope of Graph Neural Networks","created":"2022-01-19","cited_by_count":94,"area":null},{"id":"2101.05510","title":"Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond","created":"2021-01-14","cited_by_count":82,"area":null},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80,"area":null},{"id":"2102.13303","title":"Graph-based Semi-supervised Learning: A Comprehensive Review","created":"2021-02-26","cited_by_count":79,"area":null},{"id":"2103.12608","title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research","created":"2021-03-23","cited_by_count":76,"area":null},{"id":"2110.02027","title":"ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning","created":"2021-10-05","cited_by_count":72,"area":null},{"id":"2203.06714","title":"A Survey on Deep Graph Generation: Methods and Applications","created":"2022-03-13","cited_by_count":69,"area":null},{"id":"2110.05802","title":"Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform","created":"2021-10-12","cited_by_count":66,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2402.08871","title":"Position: Topological Deep Learning is the New Frontier for Relational Learning","created":"2024-02-14","cited_by_count":66,"area":null},{"id":"2207.03579","title":"DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection","created":"2022-06-30","cited_by_count":63,"area":null},{"id":"2202.08057","title":"Understanding and Improving Graph Injection Attack by Promoting Unnoticeability","created":"2022-02-16","cited_by_count":62,"area":null},{"id":"2204.07697","title":"Theory of Graph Neural Networks: Representation and Learning","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2101.01229","title":"A Survey on Embedding Dynamic Graphs","created":"2021-01-04","cited_by_count":60,"area":null},{"id":"2207.06010","title":"Does GNN Pretraining Help Molecular Representation?","created":"2022-07-13","cited_by_count":60,"area":null},{"id":"2104.09376","title":"Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training","created":"2021-04-19","cited_by_count":54,"area":null},{"id":"2406.06470","title":"GKAN: Graph Kolmogorov-Arnold Networks","created":"2024-06-10","cited_by_count":53,"area":null},{"id":"2201.07083","title":"A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants","created":"2022-01-18","cited_by_count":52,"area":null},{"id":"2112.08541","title":"BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing","created":"2021-12-16","cited_by_count":50,"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":"2203.00949","title":"GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation","created":"2022-03-02","cited_by_count":46,"area":null},{"id":"2302.04451","title":"Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion","created":"2023-02-09","cited_by_count":40,"area":null},{"id":"2103.00742","title":"Automated Machine Learning on Graphs: A Survey","created":"2021-03-01","cited_by_count":39,"area":null},{"id":"2103.04886","title":"Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks","created":"2021-03-08","cited_by_count":39,"area":null},{"id":"2102.10062","title":"A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective","created":"2021-02-19","cited_by_count":38,"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":"2307.07107","title":"Graph Positional and Structural Encoder","created":"2023-07-14","cited_by_count":33,"area":null},{"id":"2110.07317","title":"ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection","created":"2021-10-14","cited_by_count":32,"area":null},{"id":"2205.03834","title":"FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction","created":"2022-05-08","cited_by_count":31,"area":null},{"id":"2203.14082","title":"Metropolis-Hastings Data Augmentation for Graph Neural Networks","created":"2022-03-26","cited_by_count":30,"area":null},{"id":"2405.00476","title":"A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges","created":"2024-05-01","cited_by_count":30,"area":null},{"id":"2101.08358","title":"Marius: Learning Massive Graph Embeddings on a Single Machine","created":"2021-01-20","cited_by_count":29,"area":null},{"id":"2205.09702","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","created":"2022-05-19","cited_by_count":29,"area":null},{"id":"2211.04952","title":"Graph Neural Networks with Adaptive Readouts","created":"2022-11-09","cited_by_count":29,"area":null},{"id":"2104.08769","title":"Fair Representation Learning for Heterogeneous Information Networks","created":"2021-04-18","cited_by_count":28,"area":null},{"id":"2201.02534","title":"MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs","created":"2022-01-07","cited_by_count":28,"area":null},{"id":"2202.09025","title":"Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction","created":"2022-02-18","cited_by_count":28,"area":null},{"id":"2108.09513","title":"A Hard Label Black-box Adversarial Attack Against Graph Neural Networks","created":"2021-08-21","cited_by_count":26,"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":"2305.15616","title":"Reversible and irreversible bracket-based dynamics for deep graph neural networks","created":"2023-05-24","cited_by_count":26,"area":null},{"id":"2103.03330","title":"Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture","created":"2021-03-04","cited_by_count":25,"area":null},{"id":"2205.11691","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2104.09856","title":"Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning","created":"2021-04-20","cited_by_count":23,"area":null},{"id":"2104.05316","title":"Better Feature Integration for Named Entity Recognition","created":"2021-04-12","cited_by_count":22,"area":null},{"id":"2109.04352","title":"PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery","created":"2021-09-09","cited_by_count":22,"area":null},{"id":"2110.14056","title":"How to transfer algorithmic reasoning knowledge to learn new algorithms?","created":"2021-10-26","cited_by_count":22,"area":null},{"id":"2202.00961","title":"Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction","created":"2022-02-02","cited_by_count":22,"area":null},{"id":"2106.03819","title":"A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps","created":"2021-06-07","cited_by_count":21,"area":null},{"id":"2106.11863","title":"Graph coarsening: From scientific computing to machine learning","created":"2021-06-22","cited_by_count":21,"area":null},{"id":"2204.03080","title":"Graph Neural Networks Designed for Different Graph Types: A Survey","created":"2022-04-06","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":"2201.11932","title":"Deep Generative Model for Periodic Graphs","created":"2022-01-28","cited_by_count":20,"area":null},{"id":"2203.16639","title":"FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations","created":"2022-03-30","cited_by_count":20,"area":null},{"id":"2210.09766","title":"DAGAD: Data Augmentation for Graph Anomaly Detection","created":"2022-10-18","cited_by_count":20,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2109.03560","title":"X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning","created":"2021-09-08","cited_by_count":19,"area":null},{"id":"2110.07582","title":"Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding","created":"2021-10-14","cited_by_count":19,"area":null},{"id":"2307.06104","title":"Deep learning for dynamic graphs: models and benchmarks","created":"2023-07-12","cited_by_count":19,"area":null},{"id":"2101.07730","title":"A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations","created":"2021-01-19","cited_by_count":18,"area":null},{"id":"2105.10488","title":"Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery","created":"2021-05-17","cited_by_count":18,"area":null},{"id":"2106.00761","title":"Motif Prediction with Graph Neural Networks","created":"2021-05-26","cited_by_count":18,"area":null},{"id":"2108.09638","title":"Signed Bipartite Graph Neural Networks","created":"2021-08-22","cited_by_count":18,"area":null},{"id":"2208.09905","title":"MentorGNN: Deriving Curriculum for Pre-Training GNNs","created":"2022-08-21","cited_by_count":18,"area":null},{"id":"2312.10988","title":"Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization","created":"2023-12-18","cited_by_count":18,"area":null},{"id":"2103.01846","title":"The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer","created":"2021-03-02","cited_by_count":17,"area":null},{"id":"2103.04046","title":"Simplicial Complex Representation Learning","created":"2021-03-06","cited_by_count":17,"area":null},{"id":"2101.10050","title":"Learning Parametrised Graph Shift Operators","created":"2021-01-25","cited_by_count":16,"area":null},{"id":"2106.15810","title":"Edge Proposal Sets for Link Prediction","created":"2021-06-30","cited_by_count":16,"area":null},{"id":"2108.10733","title":"Graph Neural Networks: Methods, Applications, and Opportunities","created":"2021-08-24","cited_by_count":16,"area":null},{"id":"2110.12344","title":"A Broader Picture of Random-walk Based Graph Embedding","created":"2021-10-24","cited_by_count":16,"area":null},{"id":"2110.15182","title":"Dist2Cycle: A Simplicial Neural Network for Homology Localization","created":"2021-10-28","cited_by_count":16,"area":null},{"id":"2301.10857","title":"Improving Graph Generation by Restricting Graph Bandwidth","created":"2023-01-25","cited_by_count":16,"area":null},{"id":"2104.04987","title":"AutoGL: A Library for Automated Graph Learning","created":"2021-04-11","cited_by_count":15,"area":null},{"id":"2201.13410","title":"Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings","created":"2022-01-31","cited_by_count":15,"area":null},{"id":"2212.02810","title":"Data Imputation with Iterative Graph Reconstruction","created":"2022-12-06","cited_by_count":15,"area":null},{"id":"2304.05729","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","created":"2023-04-12","cited_by_count":15,"area":null},{"id":"2304.11534","title":"Graph Neural Networks for Text Classification: A Survey","created":"2023-04-23","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":"2203.10480","title":"Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey","created":"2022-03-20","cited_by_count":14,"area":null},{"id":"2211.15335","title":"You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets","created":"2022-11-28","cited_by_count":14,"area":null},{"id":"2305.13854","title":"The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey","created":"2023-05-23","cited_by_count":14,"area":null},{"id":"2407.11907","title":"GraphFM: A generalist graph transformer that learns transferable representations across diverse domains","created":"2024-07-16","cited_by_count":14,"area":null},{"id":"2410.07157","title":"InstructG2I: Synthesizing Images from Multimodal Attributed Graphs","created":"2024-10-09","cited_by_count":14,"area":null},{"id":"2503.17229","title":"FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs","created":"2025-03-21","cited_by_count":14,"area":null},{"id":"2106.06039","title":"Neural Predicting Higher-order Patterns in Temporal Networks","created":"2021-06-10","cited_by_count":13,"area":null},{"id":"2206.08561","title":"Boosting Graph Structure Learning with Dummy Nodes","created":"2022-06-17","cited_by_count":13,"area":null},{"id":"2208.06975","title":"Rethinking Graph Neural Networks for the Graph Coloring Problem","created":"2022-08-15","cited_by_count":13,"area":null},{"id":"2210.13763","title":"Teal: Learning-Accelerated Optimization of WAN Traffic Engineering","created":"2022-10-25","cited_by_count":13,"area":null},{"id":"2405.14253","title":"Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2108.01053","title":"Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders","created":"2021-08-02","cited_by_count":12,"area":null},{"id":"2206.04516","title":"Accurate Node Feature Estimation with Structured Variational Graph Autoencoder","created":"2022-06-09","cited_by_count":12,"area":null},{"id":"2402.06330","title":"Continual Learning on Graphs: A Survey","created":"2024-02-09","cited_by_count":12,"area":null},{"id":"2410.10341","title":"Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach","created":"2024-10-14","cited_by_count":12,"area":null}],"next_cursor":"100"}