diningphil / CGMMLinks
Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)
☆36Updated 3 years ago
Alternatives and similar repositories for CGMM
Users that are interested in CGMM are comparing it to the libraries listed below
Sorting:
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- PyTorch Implementation of GraphTSNE, ICLR’19☆137Updated 6 years ago
- PyTorch implementation of residual gated graph ConvNets, ICLR’18☆124Updated 7 years ago
- D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019☆145Updated 5 years ago
- Memory-Based Graph Networks☆104Updated 3 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆37Updated 5 years ago
- Learning Steady-States of Iterative Algorithms over Graphs☆40Updated 7 years ago
- LSTM implementation, and multi-layer LSTMs for learning on graph neighborhoods☆79Updated 9 years ago
- ☆44Updated 5 years ago
- Hyperbolic Graph Neural Networks☆246Updated 6 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 5 years ago
- Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)☆23Updated 2 years ago
- code for the paper in NeurIPS 2019☆40Updated 2 years ago
- IPC: A Graph Data Set Compiled from International Planning Competitions☆46Updated 6 years ago
- ☆20Updated 4 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Compute graph embeddings via Anonymous Walk Embeddings☆82Updated 7 years ago
- Code release for NeurIPS 2019 paper "End to End Learning and Optimization on Graphs"☆91Updated 6 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- ☆54Updated 3 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆53Updated 5 years ago
- Hyperbolic Hierarchical Clustering.☆206Updated 2 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- Hyperbolic Neural Networks, pytorch☆87Updated 6 years ago
- A convolutional neural network for graph classification in PyTorch☆91Updated 6 years ago
- A curated list of awesome graph representation learning.☆69Updated 5 years ago
- ☆35Updated 6 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆53Updated 6 years ago
- Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…☆135Updated 5 years ago
- Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)☆199Updated last year