kd383 / NetworkDOSLinks
Network Density of States (https://arxiv.org/abs/1905.09758) (KDD 2019)
☆25Updated 5 years ago
Alternatives and similar repositories for NetworkDOS
Users that are interested in NetworkDOS are comparing it to the libraries listed below
Sorting:
- Equivalence Between Structural Representations and Positional Node Embeddings☆22Updated 5 years ago
- Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)☆16Updated 4 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆52Updated 5 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆104Updated 5 years ago
- The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020☆34Updated 4 years ago
- Implementation of SBM-meet-GNN☆23Updated 6 years ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 4 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- Measuring and Improving the Use of Graph Information in Graph Neural Networks☆82Updated 10 months ago
- ☆35Updated 5 years ago
- Compute graph embeddings via Anonymous Walk Embeddings☆83Updated 6 years ago
- Scalable Graph Neural Networks for Heterogeneous Graphs☆72Updated 4 years ago
- Graph Feature Representation/Vector Based On The Family Of Graph Spectral Distances (NIPS 2017).☆24Updated 5 years ago
- SIGN: Scalable Inception Graph Network☆97Updated 4 years ago
- Code for Graphite iterative graph generation☆59Updated 5 years ago
- Learning Steady-States of Iterative Algorithms over Graphs☆40Updated 6 years ago
- A Persistent Weisfeiler–Lehman Procedure for Graph Classification☆61Updated 3 years ago
- A comprehensive collection of GNN works in NeurIPS 2019.☆21Updated 5 years ago
- Code for NeurIPS'19 "Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks"☆76Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆81Updated 9 months ago
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"☆22Updated 3 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 4 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆54Updated 5 years ago
- Stochastic training of graph convolutional networks☆84Updated 2 years ago
- ☆57Updated 3 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆36Updated 4 years ago
- A Python implementation of a fast approximation of the Weisfeiler-Lehman Graph Kernels.☆24Updated 5 years ago
- ☆26Updated 6 years ago