Alternatives and similar repositories for Awesome-Graph-Domain-Adaptation-Papers

Users that are interested in Awesome-Graph-Domain-Adaptation-Papers are comparing it to the libraries listed below

• THUMNLab / awesome-graph-ood
  Papers about out-of-distribution generalization on graphs.
  ☆167Updated 2 years ago
• yihongma / CILG-Papers
  A curated list of papers and code related to class-imbalanced learning on graphs (CILG).
  ☆43Updated 10 months ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆56Updated last year
• yanliang3612 / awesome-imbalanced-learning-on-graphs
  A repository contains a collection of resources and papers on Imbalance Learning On Graphs
  ☆94Updated 6 months ago
• Xtra-Computing / Awesome-Literature-ILoGs
  Awesome literature on imbalanced learning on graphs
  ☆75Updated last year
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆46Updated 3 years ago
• tengxiao1 / GraphACL
  GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
  ☆34Updated last year
• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆44Updated 2 years ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆94Updated 3 years ago
• 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
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆172Updated last year
• SitaoLuan / ACM-GNN
  NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…
  ☆87Updated last year
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆105Updated last year
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆35Updated 2 years ago
• logseminar / Schedule
  Schedule for learning on graphs seminar
  ☆109Updated 2 years ago
• yixinliu233 / D2PT
  [KDD'23] Learning Strong Graph Neural Networks with Weak Information
  ☆43Updated 2 years ago
• GSL-Benchmark / GSLB
  A comprehensive benchmark of Graph Structure Learning (NeurIPS 2023 Datasets and Benchmarks Track)
  ☆121Updated last year
• QueuQ / CGLB
  ☆59Updated last year
• Samyu0304 / graph-information-bottleneck-for-Subgraph-Recognition
  ☆57Updated 3 years ago
• Amanda-Zheng / Awesome-Data-Centric-GraphML
  A collection of papers and resources about Data Centric Graph Machine Learning (DC-GML)
  ☆40Updated 2 years ago
• Newiz430 / Pretext
  Resource for "A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective"
  ☆37Updated 6 months ago
• 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
• junxia97 / SimGRACE
  [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
  ☆81Updated 3 years ago
• yixinliu233 / GREET
  [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
  ☆53Updated 3 years ago
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆202Updated 9 months ago
• HaitaoMao / Awesome_Graph_Foundation_Models
  Accompanied repositories for our paper Graph foundation model
  ☆228Updated last year
• tengxiao1 / Awesome-Graph-Contrastive-Learning-with-Heterophily
  Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
  ☆37Updated 2 years ago
• Amanda-Zheng / Auto-HeG
  Pytorch implementation of WWW'23:"Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs"
  ☆16Updated 2 years ago
• LechengKong / OneForAll
  A fundational graph learning framework that solves cross-domain/cross-task classification problems using one model.
  ☆239Updated last year
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆92Updated 4 years ago