HongtengXu / gwlLinks
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
☆72Updated 6 years ago
Alternatives and similar repositories for gwl
Users that are interested in gwl are comparing it to the libraries listed below
Sorting:
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching☆44Updated 6 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 3 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 5 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆104Updated 5 years ago
- ☆25Updated 5 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆86Updated last year
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆20Updated 5 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆53Updated 5 years ago
- [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆111Updated 4 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆43Updated last year
- Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)☆199Updated last year
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- PyTorch Implementation of GraphTSNE, ICLR’19☆137Updated 6 years ago
- The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields☆58Updated 5 years ago
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆46Updated 4 years ago
- Statistics on the space of asymmetric networks via Gromov-Wasserstein distance☆15Updated 5 years ago
- Graph Representation Learning via Graphical Mutual Information Maximization☆117Updated 5 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- ☆37Updated 6 years ago
- Memory-Based Graph Networks☆104Updated 3 years ago
- ☆45Updated 8 years ago