Alternatives and similar repositories for pygsp_tutorial_graphsip

Users that are interested in pygsp_tutorial_graphsip are comparing it to the libraries listed below

• FilippoMB / Benchmark_dataset_for_graph_classification
  Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.
  ☆50Updated 4 months ago
• BorgwardtLab / P-WL
  A Persistent Weisfeiler–Lehman Procedure for Graph Classification
  ☆61Updated 4 years ago
• paulmorio / geo2dr
  Geo2DR: A Python and PyTorch library for learning distributed representations of graphs.
  ☆46Updated 2 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆105Updated 5 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆83Updated 11 months ago
• balcilar / Spectral-Designed-Graph-Convolutions
  Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper
  ☆50Updated 4 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆101Updated 2 years ago
• FilippoMB / Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
  Reproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Poolin…
  ☆271Updated 4 months ago
• yeze16159 / CurvGN
  Curvature Graph Network
  ☆19Updated 5 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• rodrigo-pena / graph-learning
  ☆14Updated 8 years ago
• lcosmo / DGM_pytorch
  ☆54Updated 3 years ago
• BorgwardtLab / TOGL
  Topological Graph Neural Networks (ICLR 2022)
  ☆124Updated 3 years ago
• chrsmrrs / tudataset
  ☆95Updated 2 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 5 years ago
• deezer / linear_graph_autoencoders
  Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…
  ☆133Updated 4 years ago
• 000Justin000 / gnn-residual-correlation
  ☆20Updated 4 years ago
• gasteigerjo / gdc
  Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)
  ☆273Updated 2 years ago
• leowyy / GraphTSNE
  PyTorch Implementation of GraphTSNE, ICLR’19
  ☆136Updated 6 years ago
• lucfra / LDS-GNN
  Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)
  ☆197Updated last year
• zhengdao-chen / GNN4CD
  Supervised community detection with line graph neural networks
  ☆89Updated 4 years ago
• Hermina / GOT
  Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"
  ☆41Updated last year
• 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…
  ☆142Updated 5 months ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆79Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• cvignac / SMP
  ☆28Updated 3 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 years ago
• trneedham / Spectral-Gromov-Wasserstein
  Graph matching and clustering by comparing heat kernels via optimal transport.
  ☆27Updated 2 years ago