Alternatives and similar repositories for NetworkDOS:

Users that are interested in NetworkDOS are comparing it to the libraries listed below

• TheGravLab / A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening
  Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)
  ☆16Updated 4 years ago
• vwz / AGE
  Active Learning for Graph Embedding
  ☆32Updated 7 years ago
• nikhil-dce / SBM-meet-GNN
  Implementation of SBM-meet-GNN
  ☆23Updated 5 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 8 months ago
• BorgwardtLab / P-WL
  A Persistent Weisfeiler–Lehman Procedure for Graph Classification
  ☆61Updated 3 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 years ago
• yifan-h / CS-GNN
  Measuring and Improving the Use of Graph Information in Graph Neural Networks
  ☆82Updated 9 months ago
• GraphZoom-iclr / GraphZoom
  ☆14Updated 2 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆104Updated 5 years ago
• chrsmrrs / sparsewl
  Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"
  ☆22Updated 3 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 5 years ago
• danielzuegner / robust-gcn
  Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".
  ☆43Updated 4 years ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆94Updated 3 years ago
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆43Updated 5 years ago
• twitter-research / sign
  SIGN: Scalable Inception Graph Network
  ☆96Updated 4 years ago
• balcilar / gnn-spectral-expressive-power
  Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
  ☆46Updated 3 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• gear / gfnn
  Graph Filter Neural Network (ICPR'20)
  ☆48Updated 4 years ago
• facebookresearch / NARS
  Scalable Graph Neural Networks for Heterogeneous Graphs
  ☆71Updated 4 years ago
• cshjin / gnn_in_neurips_2019
  A comprehensive collection of GNN works in NeurIPS 2019.
  ☆21Updated 5 years ago
• nd7141 / AWE
  Compute graph embeddings via Anonymous Walk Embeddings
  ☆83Updated 6 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• acbull / LADIES
  Code for NeurIPS'19 "Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks"
  ☆76Updated 2 years ago
• yunshengb / GraphSim
  ☆53Updated 5 years ago
• FFTYYY / TWIRLS
  The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".
  ☆43Updated 3 years ago
• cvignac / SMP
  ☆27Updated 3 years ago
• lorenzopalloni / wl-graph-kernels
  A Python implementation of a fast approximation of the Weisfeiler-Lehman Graph Kernels.
  ☆23Updated 5 years ago
• Hanjun-Dai / steady_state_embedding
  Learning Steady-States of Iterative Algorithms over Graphs
  ☆40Updated 6 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆22Updated 4 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 4 years ago