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

• THUMNLab / awesome-graph-ood
  Papers about out-of-distribution generalization on graphs.
  ☆166Updated last year
• usail-hkust / Awesome-Causality-Inspired-GNNs
  An awesome collection of causality-inspired graph neural networks.
  ☆69Updated 3 months ago
• yongduosui / CAL
  [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…
  ☆86Updated last year
• MingChen-Sun / GPPT
  ☆57Updated 2 years ago
• Wuyxin / DIR-GNN
  Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)
  ☆124Updated last year
• flyingdoog / PGExplainer
  Parameterized Explainer for Graph Neural Network
  ☆130Updated last year
• logseminar / Schedule
  Schedule for learning on graphs seminar
  ☆109Updated last year
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆166Updated last year
• YesLab-Code / HTGNN
  ☆52Updated 3 years ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆54Updated last year
• nuster1128 / Awesome-Causal-Papers
  We organize papers related to causal that published on top conferences recently. (因果领域论文分类汇总)
  ☆81Updated last year
• TrustAGI-Lab / SUBLIME
  [WWW'22] Towards Unsupervised Deep Graph Structure Learning
  ☆135Updated 2 years ago
• SitaoLuan / LLM4Graph
  ☆171Updated last year
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆110Updated last year
• kaize0409 / awesome-graph-data-augmentaion
  A curated list of publications and code about data augmentaion for graphs.
  ☆64Updated 2 years ago
• CongWeilin / GraphMixer
  ☆99Updated last year
• alexfanjn / Graph-Neural-Networks-With-Heterophily
  This repository contains the resources on graph neural network (GNN) considering heterophily.
  ☆247Updated 3 months ago
• GSL-Benchmark / GSLB
  A comprehensive benchmark of Graph Structure Learning (NeurIPS 2023 Datasets and Benchmarks Track)
  ☆112Updated last year
• EnyanDai / awesome-turstworthy-graph-neural-networks
  ☆24Updated 2 years ago
• DM2-ND / CFLP
  Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction
  ☆66Updated 2 years ago
• jma712 / HyperSCI
  ☆43Updated last year
• LarsHoldijk / RE-ParameterizedExplainerForGraphNeuralNetworks
  ☆55Updated 3 years ago
• flyingdoog / PTDNet
  Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification
  ☆77Updated 3 years ago
• fpour / DGB
  Dynamic Graph Benchmark
  ☆74Updated 2 years ago
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆101Updated last year
• THUMNLab / awesome-auto-graph-learning
  A paper collection about automated graph learning
  ☆97Updated 9 months ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆86Updated 2 years ago
• HaitaoMao / Awesome_Graph_Foundation_Models
  Accompanied repositories for our paper Graph foundation model
  ☆195Updated 4 months ago
• seven-echo / dynamic-graph-embedding-method
  dynamic graph/network embedding/representation methods
  ☆18Updated 4 years ago
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆192Updated last month