A collection of papers on Graph Structural Learning (GSL). We will try to make this list updated frequently. If you found any error or any missed paper, please don't hesitate to open issues or pull requests.
We have developed a comprehensive graph structure learning benchmark (GSLB), which consists of diverse graph datasets and state-of-the-art GSL algorithm. Feel free to explore our benchmark and provide any feedback or contributions.
- [TKDE 2024] Bi-Level Graph Structure Learning for Next POI Recommendation [Paper]
- [ICDE 2024] Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting [Paper]
- [ACL 2024] S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis [Paper | Code]
- [WWW 2024] DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning [Paper | Code]
- [WWW 2024] Self-Guided Robust Graph Structure Refinement [Paper | Code]
- [AAAI 2024] Neural Gaussian Similarity Modeling for Differential Graph Structure Learning [Paper]
- [NeurIPS 2023] Latent Graph Inference with Limited Supervision [Paper | Code]
- [NeurIPS 2023] Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First [Paper | Code]
- [NeurIPS 2023] Towards Label Position Bias in Graph Neural Networks [Paper]
- [ICDE 2023] Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting [Paper]
- [CIKM 2023] Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs [Paper | Code]
- [CIKM 2023] RDGSL: Dynamic Graph Representation Learning with Structure Learning [Paper]
- [CIKM 2023] Homophily-enhanced Structure Learning for Graph Clustering [Paper | Code]
- [IJCAI 2023] Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification [Paper | Code]
- [KDD 2023] PROSE: Graph Structure Learning via Progressive Strategy [Paper | Code]
- [KDD 2023] Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities [Paper | Code]
- [KDD 2023] GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [Paper | Code]
- [TNNLS 2023] Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions [Paper | Code]
- [WWW 2023] Homophily-oriented Heterogeneous Graph Rewiring [Paper]
- [WWW 2023] SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization [Paper | Code]
- [ICDE 2023] Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport [Paper | Code]
- [WSDM 2023] Self-Supervised Graph Structure Refinement for Graph Neural Networks [Paper | Code]
- [AAAI 2023] Directed Acyclic Graph Structure Learning from Dynamic Graphs [Paper | Code]
- [AAAI 2023] Self-organization Preserved Graph Structure Learning with Principle of Relevant Information [Paper ｜ Code]
- [AAAI 2023] USER: Unsupervised Structural Entropy-based Robust Graph Neural Network [Paper | Code]
- [AAAI 2023] Spatio-Temporal Meta-Graph Learning for Traffic Forecasting [Paper | Code]
- [TPAMI 2023] Differentiable Graph Module (DGM) for Graph Convolutional Networks [Paper | Code]
- [TNNLS 2022] Reverse graph learning for graph neural network [Paper]
- [NeurIPS 2022] Contrastive Graph Structure Learning via Information Bottleneck for Recommendation [Paper | Code]
- [NeurIPS 2022] NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [Paper | Code]
- [NeurIPS 2022] Simultaneous Missing Value Imputation and Structure Learning with Groups [Paper | Code]
- [CIKM 2022] Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing [Paper | Code]
- [KDD 2022] Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification [Paper]
- [KDD 2022] Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN [Paper | Code]
- [ICML 2022] Boosting graph structure learning with dummy nodes [Paper | Code]
- [WWW 2022] Towards Unsupervised Deep Graph Structure Learning [Paper | Note | Code]
- [WWW 2022] Compact Graph Structure Learning via Mutual Information Compression [Paper | Code]
- [WWW 2022] Prohibited Item Detection via Risk Graph Structure Learning [Paper]
- [WSDM 2022] Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels [Paper | Code]
- [AAAI 2022] GPN: A Joint Structural Learning Framework for Graph Neural Networks [Paper]
- [AAAI 2022] Graph Structure Learning with Variational Information Bottleneck [Paper | Code]
- [arXiv 2022] GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks [Paper | Note]
- [IJCAI 2022] Hypergraph Structure Learning for Hypergraph Neural Networks [Paper]
- [IJCAI 2022] Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting [Paper | Code]
- [ICLR 2022] Understanding over-squashing and bottlenecks on graphs via curvature [Paper]
- [arXiv 2022] A Survey on Graph Structure Learning: Progress and Opportunities [Paper]
- [NeurIPS 2021] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [Paper | Note | Code]
- [WWW 2021] Graph Structure Estimation Neural Networks [Paper | Code]
- [CIKM 2021] Speedup Robust Graph Structure Learning with Low-Rank Information [Paper]
- [WSDM 2021] Learning to Drop: Robust Graph Neural Network via Topological Denoising [Paper | Note | Code]
- [WSDM 2021] Node Similarity Preserving Graph Convolutional Networks [Paper | Code]
- [IJCAI 2021] Understanding Structural Vulnerability in Graph Convolutional Networks [Paper | Code]
- [ECML-PKDD 2021] Graph-Revised Convolutional Network [Paper | Note | Code]
- [arXiv 2021] A General Unified Graph Neural Network Framework Against Adversarial Attacks [Paper]
- [AAAI 2021] Heterogeneous Graph Structure Learning for Graph Neural Networks [Paper | Code]
- [ICLR 2021] Discrete Graph Structure Learning for Forecasting Multiple Time Series [Paper | Code]
- [IoTJ 2021] Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT [Paper | Code]
- [TNNLS 2020] Probabilistic semi-supervised learning via sparse graph structure learning [Paper]
- [ICML 2020] Robust Graph Representation Learning via Neural Sparsification [Paper | Code]
- [NeurIPS 2020] Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings [Paper | Note | Code]
- [NeurIPS 2020] GNNGUARD: Defending Graph Neural Networks against Adversarial Attacks [Paper]
- [NeurIPS 2020] Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting [Paper | Code]
- [KDD 2020] Graph Structure Learning for Robust Graph Neural Networks [Paper | Code]
- [KDD 2020] Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [Paper |Code]
- [WSDM 2020] All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs [Paper | Note]
- [ICDM 2020] Provably Robust Node Classification via Low-Pass Message Passing [Paper]
- [CIKM 2020] Data Augmentation for Graph Classification [Paper | Code]
- [ICML 2019] Learning Discrete Structures for Graph Neural Networks [Paper | Note | Code]
- [KDD 2018] Adversarial Attacks on Neural Networks for Graph Data [Paper | Code]
- [ICDM 2019] Learning Robust Representations with Graph Denoising Policy Network [Paper]
- [IJCAI 2019] Adversarial Examples for Graph Data: Deep Insights into Attack and Defense [Paper]
- [CVPR 2019] Semi-supervised Learning with Graph Learning-Convolutional Networks [Paper]
- [AAAI 2018] Adaptive Graph Convolutional Neural Networks [Paper]
- [ICML 2018] Neural Relational Inference for Interacting Systems [Paper | Code]
If you have come across relevant resources, feel free to open an issue or submit a pull request.
* [conference] **paper_name** [[Paper](link) | [Code](link)]