Alternatives and similar repositories for algorithmic-concepts-reasoning:

Users that are interested in algorithmic-concepts-reasoning are comparing it to the libraries listed below

• cvignac / SMP
  ☆27Updated 3 years ago
• PurdueMINDS / size-invariant-GNNs
  Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)
  ☆23Updated 2 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆22Updated 4 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 4 years ago
• 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
• chrsmrrs / sparsewl
  Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"
  ☆22Updated 3 years ago
• GentleZhu / Shift-Robust-GNNs
  "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
  ☆47Updated 2 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆30Updated last year
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆94Updated 2 years ago
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆53Updated last year
• PurdueMINDS / RelationalPooling
  ☆35Updated 5 years ago
• stadlmax / Graph-Posterior-Network
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
  ☆42Updated 2 years ago
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 4 years ago
• beabevi / ESAN
  Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
  ☆87Updated last year
• balcilar / Spectral-Designed-Graph-Convolutions
  Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper
  ☆50Updated 4 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 years ago
• AaltoPML / Rethinking-pooling-in-GNNs
  ☆62Updated 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
• haotang1995 / IterGNN
  Code for "Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs" (NeurIPS 2020)
  ☆18Updated 4 years ago
• FilippoMB / The-expressive-power-of-pooling-in-GNNs
  Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023
  ☆37Updated last year
• G-Taxonomy-Workgroup / GPSE
  Graph Positional and Structural Encoder
  ☆46Updated 2 months ago
• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆72Updated 5 years ago
• luis-mueller / probing-graph-transformers
  Code for our paper "Attending to Graph Transformers"
  ☆85Updated last year
• mims-harvard / G-Meta
  Graph meta learning via local subgraphs (NeurIPS 2020)
  ☆121Updated 8 months ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆36Updated 3 years ago
• leichen2018 / GNN-Substructure-Counting
  The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020
  ☆34Updated 4 years ago
• ispamm / MATE
  MetA-Train to Explain
  ☆17Updated 3 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• DevinKreuzer / SAN
  ☆155Updated 3 years ago
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆66Updated 3 years ago