Alternatives and similar repositories for gnn-residual-correlation

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

• JunwenBai / correlation-gnn
  Correlated Graph Neural Networks
  ☆27Updated 5 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• nikhil-dce / SBM-meet-GNN
  Implementation of SBM-meet-GNN
  ☆23Updated 6 years ago
• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 4 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 5 years ago
• cvignac / SMP
  ☆28Updated 3 years ago
• pbielak / graph-barlow-twins
  The official implementation of the Graph Barlow Twins method with the experimental pipeline
  ☆31Updated last year
• rsonthal / TreeRep
  Learning Tree structures and Tree metrics
  ☆23Updated 9 months ago
• 000Justin000 / ssl_edge
  Code for "Graph-based Semi-Supervised & Active Learning for Edge Flows"
  ☆15Updated 6 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆31Updated 2 years ago
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆22Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 9 months ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 5 years ago
• shchur / triangular-tpp
  Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
  ☆23Updated last year
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• FilippoMB / Total-variation-graph-neural-networks
  Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.
  ☆20Updated 2 months ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆35Updated 3 years ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated last year
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆43Updated 5 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 5 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• jliu / graph-normalizing-flows
  Code for Graph Normalizing Flows.
  ☆61Updated 5 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆104Updated 5 years ago
• StatsDLMathsRecomSys / Generative-Graph-Convolutional-Network-for-Growing-Graphs
  ☆12Updated 5 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆23Updated 4 years ago
• Hermina / GOT
  Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"
  ☆41Updated last year