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
  ☆28Updated 3 years ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆95Updated 3 years ago
• beabevi / ESAN
  Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
  ☆89Updated 2 years ago
• PurdueMINDS / size-invariant-GNNs
  Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)
  ☆23Updated 2 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 5 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆31Updated 2 years ago
• chrsmrrs / sparsewl
  Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"
  ☆22Updated 3 years ago
• DevinKreuzer / SAN
  ☆155Updated 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 4 years ago
• inria-thoth / GraphiT
  Official Pytorch Implementation of GraphiT
  ☆109Updated 4 years ago
• mims-harvard / G-Meta
  Graph meta learning via local subgraphs (NeurIPS 2020)
  ☆126Updated last year
• Saro00 / DGN
  Implementation of Directional Graph Networks in PyTorch and DGL
  ☆118Updated 4 years ago
• vijaydwivedi75 / lrgb
  Long Range Graph Benchmark, NeurIPS 2022 Track on D&B
  ☆158Updated last year
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆53Updated 2 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
  ☆36Updated last year
• se-ok / WLsimilarity
  informal exposition of Weisfeiler-Leman similarity
  ☆28Updated 4 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 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 5 years ago
• AaltoPML / Rethinking-pooling-in-GNNs
  ☆62Updated 4 years ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆36Updated 3 years ago
• G-Taxonomy-Workgroup / GPSE
  Graph Positional and Structural Encoder
  ☆53Updated 7 months ago
• KarolisMart / DropGNN
  The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)
  ☆26Updated 3 years ago
• jctops / understanding-oversquashing
  ☆41Updated 3 years ago
• mlelarge / graph_neural_net
  Expressive Power of Invariant and Equivariant Graph Neural Networks (ICLR 2021)
  ☆40Updated 2 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆83Updated last year
• noamrazin / gnn_interactions
  Code for reproducing experiments in "On the Ability of Graph Neural Networks to Model Interactions Between Vertices"
  ☆25Updated last year
• leichen2018 / GNN-Substructure-Counting
  The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020
  ☆34Updated 4 years ago
• uoguelph-mlrg / GGM-metrics
  Official repository for the paper "On Evaluation Metrics for Graph Generative Models"
  ☆25Updated 3 years ago
• MichailChatzianastasis / GOAT
  Official Code Repository for the paper "Graph Ordering Attention Networks"
  ☆21Updated last year
• Wuyxin / ReFine
  Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods
  ☆70Updated 7 months ago