jwcalder / GraphLearning

Related projects: ⓘ

• FilippoMB / Benchmark_dataset_for_graph_classification
  This repository contains a dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.
  ☆46Updated last year
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆77Updated 2 weeks ago
• ngoc-nguyen-0 / DGG
  Linxiao Yang, Ngai-Man Cheung, Jiaying Li, and Jun Fang, "Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embeddi…
  ☆48Updated 4 years ago
• sharpenb / Differentiable-DAG-Sampling
  Differentiable DAG Sampling (ICLR 2022)
  ☆35Updated 2 years ago
• yincheng / GGP
  Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'
  ☆38Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆30Updated 3 years ago
• FabianFalck / mfcvae
  Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of var…
  ☆38Updated last year
• lucfra / LDS-GNN
  Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)
  ☆190Updated 6 months ago
• zxj32 / uncertainty-GNN
  Uncertainty Aware Semi-Supervised Learning on Graph Data
  ☆39Updated 3 years ago
• kurowasan / GraN-DAG
  ☆86Updated last year
• mori97 / VaDE
  A PyTorch Implementation of VaDE(https://arxiv.org/pdf/1611.05148.pdf)
  ☆32Updated 3 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆95Updated last year
• deezer / linear_graph_autoencoders
  Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…
  ☆132Updated 3 years ago
• topology474 / WKPI
  WKPI: A kernel based on persistent homology
  ☆12Updated 4 years ago
• paulmorio / geo2dr
  Geo2DR: A Python and PyTorch library for learning distributed representations of graphs.
  ☆44Updated last year
• BorgwardtLab / TOGL
  Topological Graph Neural Networks (ICLR 2022)
  ☆109Updated 2 years ago
• BorisMuzellec / MissingDataOT
  A Pytorch implementation of missing data imputation using optimal transport.
  ☆93Updated 3 years ago
• Hermina / GOT
  Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"
  ☆38Updated 10 months ago
• mdeff / pygsp_tutorial_graphsip
  Graph signal processing tutorial, presented at the GraphSiP summer school.
  ☆71Updated 5 years ago
• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆70Updated 5 years ago
• nairouz / DynAE
  ☆30Updated 3 years ago
• lcosmo / DGM_pytorch
  ☆48Updated 2 years ago
• AaltoPML / Rethinking-pooling-in-GNNs
  ☆60Updated 3 years ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆91Updated 2 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆56Updated 5 years ago
• muhanzhang / D-VAE
  D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019
  ☆125Updated 3 years ago
• armanihm / GDC
  Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
  ☆53Updated 4 years ago
• vunhatminh / PGMExplainer
  Generating PGM Explanation for GNN predictions
  ☆72Updated last year
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆100Updated 4 years ago
• BorgwardtLab / P-WL
  A Persistent Weisfeiler–Lehman Procedure for Graph Classification
  ☆60Updated 3 years ago