Alternatives and similar repositories for correlation-gnn

Users that are interested in correlation-gnn are comparing it to the libraries listed below

• 000Justin000 / gnn-residual-correlation
  ☆20Updated 4 years ago
• balcilar / gnn-spectral-expressive-power
  Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
  ☆46Updated 4 years ago
• pbielak / graph-barlow-twins
  The official implementation of the Graph Barlow Twins method with the experimental pipeline
  ☆31Updated 2 years ago
• twitter-research / feature-propagation
  ☆84Updated 3 years ago
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆55Updated 2 years ago
• armanihm / GDC
  Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
  ☆61Updated 5 years ago
• RPaolino / fLode
  ☆18Updated last year
• vunhatminh / PGMExplainer
  Generating PGM Explanation for GNN predictions
  ☆75Updated 2 years ago
• AaltoPML / Rethinking-pooling-in-GNNs
  ☆62Updated 4 years ago
• SherylHYX / pytorch_geometric_signed_directed
  PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepte…
  ☆143Updated 8 months ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆85Updated last year
• DeepGraphLearning / ContinuousGNN
  ☆47Updated 3 years ago
• hazdzz / awesome-gnn
  A list for GNNs and related works.
  ☆99Updated 3 months ago
• FilippoMB / The-expressive-power-of-pooling-in-GNNs
  Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023
  ☆36Updated last year
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆97Updated 3 years ago
• AaltoPML / PINT
  ☆19Updated 2 years ago
• Chen-Cai-OSU / GNN-Over-Smoothing
  A Note On Over-Smoothing for Graph Neural Network
  ☆20Updated 5 years ago
• wokas36 / GraphSNN
  Graph Structured Neural Network
  ☆40Updated 3 years ago
• KarolisMart / DropGNN
  The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)
  ☆26Updated 3 years ago
• cvignac / SMP
  ☆28Updated 4 years ago
• calvin-zcx / ndcn
  Neural Dynamics on Complex Networks
  ☆54Updated 5 years ago
• hans66hsu / GATS
  Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)
  ☆24Updated 4 months ago
• FilippoMB / Benchmark_dataset_for_graph_classification
  Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.
  ☆50Updated 7 months ago
• GraphFramEx / graphframex
  GraphFramEx: a systematic evaluation framework for explainability methods on GNNs
  ☆46Updated last year
• maxwass / pyGSL
  A Graph Structure Learning (GSL) Toolkit
  ☆36Updated 2 years ago
• facebookresearch / neural_stpp
  Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…
  ☆104Updated 3 years ago
• snap-stanford / conformalized-gnn
  Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
  ☆83Updated 2 years ago
• tk-rusch / GraphCON
  GraphCON (ICML 2022)
  ☆59Updated 3 years ago
• jliu / graph-normalizing-flows
  Code for Graph Normalizing Flows.
  ☆63Updated 5 years ago
• facebookresearch / shaDow_GNN
  [NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architec…
  ☆137Updated 3 years ago