Alternatives and similar repositories for A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening:

Users that are interested in A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening are comparing it to the libraries listed below

• chrsmrrs / sparsewl
  Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"
  ☆22Updated 3 years ago
• cvignac / SMP
  ☆26Updated 3 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 4 months ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆22Updated 4 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 5 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
  ☆45Updated 3 years ago
• zekarias-tilahun / SelfGNN
  A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in …
  ☆30Updated 3 years ago
• GentleZhu / Shift-Robust-GNNs
  "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
  ☆47Updated 2 years ago
• HekpoMaH / algorithmic-concepts-reasoning
  ☆25Updated 3 years ago
• balcilar / Spectral-Designed-Graph-Convolutions
  Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper
  ☆50Updated 3 years ago
• leichen2018 / GNN-Substructure-Counting
  The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020
  ☆34Updated 3 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆35Updated 4 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 4 years ago
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆53Updated last year
• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆72Updated 5 years ago
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 4 years ago
• Chen-Cai-OSU / GNN-Over-Smoothing
  A Note On Over-Smoothing for Graph Neural Network
  ☆19Updated 4 years ago
• Namkyeong / BGRL_Pytorch
  Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
  ☆78Updated 3 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 4 years ago
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆42Updated 5 years ago
• nerdslab / bgrl
  PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)
  ☆81Updated last year
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆94Updated 2 years ago
• vikasverma1077 / GraphMix
  Code for reproducing results in GraphMix paper
  ☆72Updated 2 years ago
• kd383 / NetworkDOS
  Network Density of States (https://arxiv.org/abs/1905.09758) (KDD 2019)
  ☆25Updated 5 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆51Updated 4 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆30Updated last year
• Graph-COM / SUREL
  [VLDB'22] SUREL is a novel walk-based computation framework for efficient subgraph-based graph representation learning.
  ☆19Updated 2 years ago
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆21Updated 4 years ago
• mengliu1998 / DL4DisassortativeGraphs
  Papers about developing DL methods on disassortative graphs
  ☆48Updated 2 years ago
• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago