/awesome-graph-structure-learning

A collection of papers on Graph Structural Learning (GSL)

awesome-graph-structure-learning

Awesome

A collection of papers on Graph Structural Learning (GSL). Will be frequently updated.

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.

2023

  • [ICDE 2023] Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting [Paper]
  • [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]

2022

  • [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 | 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]
  • [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]

2021

  • [NeurIPS 2021] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [Paper | 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 | 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 | 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]

2020

  • [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 | 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]
  • [ICDM 2020] Provably Robust Node Classification via Low-Pass Message Passing [Paper]
  • [CIKM 2020] Data Augmentation for Graph Classification [Paper | Code]

Before 2020

  • [ICML 2019] Learning Discrete Structures for Graph Neural Networks [Paper | 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]

Contributing

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)]