Skip to content

Latest commit

 

History

History
38 lines (29 loc) · 3.81 KB

File metadata and controls

38 lines (29 loc) · 3.81 KB

Awesome Graph Structure Learning

A collection of research papers related to Graph Structure Learning(GSL).

Survey

  • Deep Graph Structure Learning for Robust Representations: A Survey [Paper]

Book

  • Deep learning with graph-structured representations[Paper]

Paper

  • Graph Structure Learning for Robust Graph Neural Networks[Paper] [Code]
  • Heterogeneous Graph Structure Learning for Graph Neural Networks [Paper] [Code]
  • SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [Paper] [Code]
  • Compact Graph Structure Learning via Mutual Information Compression [Paper] [Code]
  • GNNGUARD: Defending Graph Neural Networks against Adversarial Attacks [Paper] [Code]
  • Semi-supervised Learning with Graph Learning-Convolutional Networks [Paper]
  • Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings [Paper] [Code]
  • Latent Patient Network Learning for Automatic Diagnosis [Paper]
  • Data Augmentation for Graph Neural Networks [Paper] [Code]
  • AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [Paper] [Code]
  • Graph Structure Learning with Variational Information Bottleneck [Paper]
  • Probabilistic Semi-Supervised Learning via Sparse Graph Structure Learning [Paper]
  • Label-informed Graph Structure Learning for Node Classification [Paper]
  • Multi-view graph structure learning using subspace merging on Grassmann manifold [Paper]
  • Towards Unsupervised Deep Graph Structure Learning [Paper] [Code]
  • Prohibited Item Detection via Risk Graph Structure Learning [Paper]
  • Structural Entropy Guided Graph Hierarchical Pooling [Paper]
  • Boosting Graph Structure Learning with Dummy Nodes [Paper] [Code]
  • ASGNN: Graph Neural Networks with Adaptive Structure [Paper]
  • DAG-GNN: DAG Structure Learning with Graph Neural Networks [Paper] [Code]
  • Discrete graph structure learning for forecasting multiple time series [Paper]