Alternatives and similar repositories for SGIR

Users that are interested in SGIR are comparing it to the libraries listed below

• Samyu0304 / graph-information-bottleneck-for-Subgraph-Recognition
  ☆56Updated 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
• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆44Updated 2 years ago
• junxia97 / SimGRACE
  [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
  ☆81Updated 3 years ago
• SongtaoLiu0823 / LAGNN
  [ICML 2022] Local Augmentation for Graph Neural Networks
  ☆65Updated last year
• DeepGraphLearning / GraphLoG
  Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).
  ☆80Updated 4 years ago
• googlebaba / DisC
  NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
  ☆42Updated 2 years ago
• ChandlerBang / DeCorr
  [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"
  ☆45Updated last year
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆56Updated last year
• snap-research / linkless-link-prediction
  [ICML 2023] Linkless Link Prediction via Relational Distillation
  ☆24Updated 2 years ago
• tengxiao1 / GraphACL
  GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
  ☆33Updated last year
• yuwvandy / DPGNN
  ☆38Updated 2 years ago
• JiaruiFeng / KP-GNN
  Source code for how powerful are K-hop message passing graph neural networks (Neurips 2022)
  ☆63Updated last year
• weitianxin / HyperGCL
  [NeurIPS 2022] "Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative" by Tianxin Wei, Yuning You, Tianlong Chen, Y…
  ☆61Updated 3 years ago
• meettyj / NOSMOG
  Code & data for ICLR'23 Spotlight paper "Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency".
  ☆33Updated 2 years ago
• RinneSz / GMAE
  Graph Masked Autoencoders
  ☆27Updated 3 years ago
• EdisonLeeeee / MaskGAE
  [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
  ☆89Updated last year
• Graph-COM / PEG
  ☆42Updated 2 years ago
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆171Updated last year
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆35Updated 2 years ago
• zaixizhang / ProtGNN
  Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"
  ☆51Updated 3 years ago
• LarsHoldijk / RE-ParameterizedExplainerForGraphNeuralNetworks
  ☆55Updated 3 years ago
• Shen-Lab / GraphCL_Automated
  [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…
  ☆115Updated last year
• YangLing0818 / VQGraph
  [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
  ☆102Updated last year
• yueliu1999 / KAN4Graph
  Kolmogorov Arnold Networks (KANs) for Graph Neural Networks (GNNs) and Tasks on Graphs
  ☆64Updated last year
• divelab / LECI
  The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS…
  ☆22Updated last year
• null-xyj / CoBFormer
  Implementation of ICML'24 Paper "Less is More: on the Over-Globalizing Problem in Graph Transformers"
  ☆46Updated last year
• LFhase / GALA
  [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
  ☆22Updated last year
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆105Updated 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