mdeff / pygsp_tutorial_graphsipLinks
Graph signal processing tutorial, presented at the GraphSiP summer school.
☆77Updated 6 years ago
Alternatives and similar repositories for pygsp_tutorial_graphsip
Users that are interested in pygsp_tutorial_graphsip are comparing it to the libraries listed below
Sorting:
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Topological Graph Neural Networks (ICLR 2022)☆122Updated 3 years ago
- Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.☆50Updated 6 months ago
- ☆14Updated 8 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆37Updated 5 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆42Updated last year
- Graph matching and clustering by comparing heat kernels via optimal transport.☆27Updated 2 years ago
- A Persistent Weisfeiler–Lehman Procedure for Graph Classification☆63Updated 4 years ago
- code for the paper in NeurIPS 2019☆40Updated 2 years ago
- Laplacian Change Point Detection for Dynamic Graphs (KDD 2020)☆29Updated 2 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- ☆55Updated 3 years ago
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- Graph Signal Processing in Python☆513Updated 3 weeks ago
- Reproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Poolin…☆273Updated 6 months ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- Curvature Graph Network☆19Updated 5 years ago
- Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…☆134Updated 4 years ago
- 🕸 Implementations of graph signal processing techniques from "The emerging field of signal processing on graphs" (2013)☆33Updated 6 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 6 years ago
- PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepte…☆143Updated 7 months ago
- PyTorch Implementation of GraphTSNE, ICLR’19☆137Updated 6 years ago
- A Graph Structure Learning (GSL) Toolkit☆36Updated 2 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆84Updated last year
- Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)☆272Updated 2 years ago
- Correlated Graph Neural Networks☆27Updated 5 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆96Updated 3 years ago
- Geo2DR: A Python and PyTorch library for learning distributed representations of graphs.☆46Updated 2 years ago