Alternatives and similar repositories for Awesome-Graph-Causal-Learning

Users that are interested in Awesome-Graph-Causal-Learning are comparing it to the libraries listed below

• usail-hkust / Awesome-Causality-Inspired-GNNs
  An awesome collection of causality-inspired graph neural networks.
  ☆102Updated last year
• yongduosui / CAL
  [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…
  ☆97Updated 2 years ago
• THUMNLab / awesome-graph-ood
  Papers about out-of-distribution generalization on graphs.
  ☆168Updated 2 years ago
• Wuyxin / DIR-GNN
  (ICLR 2022) Discovering Invariant Rationales for Graph Neural Networks
  ☆131Updated 2 years ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆58Updated 2 years ago
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆174Updated last year
• EdisonLeeeee / Awesome-Fair-Graph-Learning
  Paper List for Fair Graph Learning (FairGL).
  ☆144Updated last year
• jma712 / HyperSCI
  ☆45Updated last year
• YesLab-Code / HTGNN
  ☆67Updated 4 years ago
• chirag-agarwall / nifty
  Code for paper https://arxiv.org/abs/2102.13186
  ☆42Updated 4 years ago
• Skyorca / Awesome-Graph-Domain-Adaptation-Papers
  Published papers focusing on graph domain adaptation, with survey paper online as Domain Adaptation for Graph Representation Learning: Ch…
  ☆53Updated 9 months ago
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆121Updated 2 years ago
• TrustAGI-Lab / SUBLIME
  [WWW'22] Towards Unsupervised Deep Graph Structure Learning
  ☆145Updated 3 years ago
• MingChen-Sun / GPPT
  ☆63Updated 3 years ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆92Updated 3 years ago
• jma712 / GraphCFE
  ☆20Updated 8 months ago
• kaize0409 / awesome-graph-data-augmentaion
  A curated list of publications and code about data augmentaion for graphs.
  ☆63Updated 3 years ago
• YuanchenBei / Awesome-Graph-Structure-Learning
  A curated list of papers on graph structure learning (GSL).
  ☆53Updated last year
• DM2-ND / CFLP
  Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction
  ☆71Updated 3 years ago
• GSL-Benchmark / GSLB
  A comprehensive benchmark of Graph Structure Learning (NeurIPS 2023 Datasets and Benchmarks Track)
  ☆123Updated last year
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆207Updated 11 months ago
• doujiang-zheng / Awesome-Graph-Learning-Papers-List
  Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
  ☆232Updated 2 months ago
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆104Updated last year
• zyzisastudyreallyhardguy / Graph-Group-Discrimination
  Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…
  ☆56Updated 2 years ago
• logseminar / Schedule
  Schedule for learning on graphs seminar
  ☆109Updated 2 years ago
• CongWeilin / GraphMixer
  ☆104Updated 2 years ago
• GemsLab / H2GCN
  Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
  ☆107Updated 7 months ago
• LarsHoldijk / RE-ParameterizedExplainerForGraphNeuralNetworks
  ☆57Updated 3 years ago
• zaixizhang / ProtGNN
  Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"
  ☆50Updated 3 years ago
• QueuQ / CGLB
  ☆59Updated last year