Alternatives and similar repositories for Graph-Posterior-Network

Users that are interested in Graph-Posterior-Network are comparing it to the libraries listed below

• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆68Updated 3 years ago
• armanihm / GDC
  Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
  ☆61Updated 5 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 6 months ago
• GemsLab / H2GCN
  Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
  ☆106Updated 3 months ago
• mengliu1998 / DL4DisassortativeGraphs
  Papers about developing DL methods on disassortative graphs
  ☆48Updated 3 years ago
• BorealisAI / SLAPS-GNN
  PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
  ☆89Updated 3 years ago
• ChandlerBang / GCond
  [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"
  ☆141Updated 11 months 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
• Wuyxin / ReFine
  Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods
  ☆70Updated 7 months ago
• snap-stanford / GIB
  Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs
  ☆137Updated 2 years ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆96Updated 3 years ago
• flyingdoog / PGExplainer
  Parameterized Explainer for Graph Neural Network
  ☆139Updated last year
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆169Updated last year
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆116Updated 2 years ago
• guoji-fu / pGNNs
  [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks
  ☆27Updated last month
• jianhao2016 / GPRGNN
  ☆136Updated 2 years ago
• KarolisMart / DropGNN
  The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)
  ☆26Updated 3 years ago
• vijaydwivedi75 / gnn-lspe
  Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
  ☆264Updated 3 years ago
• DevinKreuzer / SAN
  ☆155Updated 4 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆84Updated last year
• LarsHoldijk / RE-ParameterizedExplainerForGraphNeuralNetworks
  ☆55Updated 3 years ago
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆202Updated 7 months ago
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆90Updated 3 years ago
• muhanzhang / NestedGNN
  ☆58Updated 3 years ago
• ChandlerBang / GTrans
  [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
  ☆61Updated 2 years ago
• yunshengb / UGraphEmb
  ☆49Updated 6 years ago
• vijaydwivedi75 / lrgb
  Long Range Graph Benchmark, NeurIPS 2022 Track on D&B
  ☆159Updated last year
• joisino / random-features
  Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)
  ☆22Updated 4 years ago
• CheriseZhu / GIL
  The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)
  ☆46Updated 4 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆23Updated 4 years ago