Alternatives and similar repositories for Graph_AE

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

• 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
• SwiftieH / IGNN
  Implicit Graph Neural Networks
  ☆62Updated 3 years ago
• danielegrattarola / SRC
  Code for "Understanding Pooling in Graph Neural Networks" (TNNLS 2022).
  ☆62Updated 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 4 years ago
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆53Updated 2 years ago
• armanihm / GDC
  Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
  ☆58Updated 4 years ago
• lcosmo / DGM_pytorch
  ☆54Updated 2 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 8 months ago
• danielegrattarola / decimation-pooling
  Code for "Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling" (TNNLS 2020).
  ☆23Updated 4 years ago
• AaltoPML / Rethinking-pooling-in-GNNs
  ☆62Updated 4 years ago
• ppope / explain_graphs
  Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …
  ☆34Updated 2 months ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆94Updated 3 years ago
• WanyuGroup / CVPR2022-OrphicX
  Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…
  ☆34Updated 3 years ago
• FilippoMB / Simplifying-Clustering-with-Graph-Neural-Networks
  Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neur…
  ☆34Updated 2 weeks ago
• zxj32 / uncertainty-GNN
  Uncertainty Aware Semi-Supervised Learning on Graph Data
  ☆40Updated 4 years ago
• leichen2018 / GNN-Substructure-Counting
  The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020
  ☆34Updated 4 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆76Updated 5 years ago
• dms-net / scatteringGCN
  Scattering GCN: overcoming oversmoothness in graph convolutional networks
  ☆25Updated 2 years ago
• KarolisMart / DropGNN
  The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)
  ☆25Updated 2 years ago
• pbielak / graph-barlow-twins
  The official implementation of the Graph Barlow Twins method with the experimental pipeline
  ☆31Updated last year
• yuanqidu / GraphGT
  ☆60Updated 2 years ago
• tk-rusch / GraphCON
  GraphCON (ICML 2022)
  ☆59Updated 2 years ago
• balcilar / Spectral-Designed-Graph-Convolutions
  Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper
  ☆50Updated 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
• davidbuterez / gnn-neural-readouts
  ☆27Updated 4 months ago
• cvignac / SMP
  ☆27Updated 3 years ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated last year
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆66Updated 3 years ago
• joisino / random-features
  Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)
  ☆22Updated 4 years ago
• FilippoMB / Benchmark_dataset_for_graph_classification
  Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.
  ☆49Updated 2 months ago