qitianwu / GraphOOD-EERM

Related projects ⓘ

Alternatives and complementary repositories for GraphOOD-EERM

• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆90Updated 8 months ago
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆101Updated last year
• BUPT-GAMMA / Graph-Structure-Estimation-Neural-Networks
  Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"
  ☆58Updated 3 years ago
• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆39Updated last year
• Graph-COM / StruRW
  [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
  ☆21Updated last year
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆42Updated 2 years ago
• Wuyxin / DIR-GNN
  Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)
  ☆120Updated last year
• nerdslab / bgrl
  PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)
  ☆80Updated last year
• yixinliu233 / G-OOD-D
  [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
  ☆35Updated last year
• RingBDStack / VIB-GSL
  Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022
  ☆32Updated 2 years ago
• qitianwu / GraphOOD-GNNSafe
  The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
  ☆72Updated last year
• THUMNLab / awesome-graph-ood
  Papers about out-of-distribution generalization on graphs.
  ☆155Updated last year
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆84Updated 2 years ago
• GraphPKU / JacobiConv
  How Powerful are Spectral Graph Neural Networks
  ☆70Updated last year
• jma712 / GraphCFE
  ☆15Updated 8 months ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆50Updated 10 months ago
• ChandlerBang / GCond
  [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"
  ☆125Updated last week
• hengruizhang98 / CCA-SSG
  Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484
  ☆68Updated 11 months ago
• QueuQ / CGLB
  ☆49Updated last year
• MingChen-Sun / GPPT
  ☆54Updated 2 years ago
• ChandlerBang / DeCorr
  [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"
  ☆43Updated 10 months ago
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆26Updated last year
• SongtaoLiu0823 / LAGNN
  [ICML 2022] Local Augmentation for Graph Neural Networks
  ☆66Updated 5 months ago
• yongduosui / AIA
  [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…
  ☆16Updated last year
• ChandlerBang / GTrans
  [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
  ☆53Updated last year
• zjs123 / DTGB
  Comprehensive Benchmark Dataset for Dynamic Text-Attributed Graphs
  ☆22Updated this week
• mengliu1998 / DL4DisassortativeGraphs
  Papers about developing DL methods on disassortative graphs
  ☆48Updated 2 years ago
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆159Updated 8 months ago
• EnyanDai / RSGNN
  An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))
  ☆32Updated 2 years ago
• yongduosui / CAL
  [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…
  ☆77Updated 11 months ago