Alternatives and similar repositories for GESS

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

• Graph-COM / LRI
  [ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.
  ☆23Updated 2 years ago
• syr-cn / SimSGT
  [NeurIPS 2023] "Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules"
  ☆37Updated last year
• zhao-ht / GIMLET
  The code for GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
  ☆62Updated last year
• Graph-COM / Pair-Align
  [ICML 2024] Code for Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)
  ☆14Updated last year
• ChnQ / LLM4Mol
  Code implementation for paper "Can Large Language Models Empower Molecular Property Prediction?"
  ☆39Updated 2 years ago
• syr-cn / ReLM
  [EMNLP 2023] ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction.
  ☆21Updated last year
• RexYing / Rex-Ying-Thesis
  Rex Ying's Ph.D. Thesis, Stanford University
  ☆41Updated 3 years ago
• liugangcode / GREA
  [KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"
  ☆45Updated 4 months ago
• Namkyeong / CMRL
  The official source code for "Shift-Robust Molecular Relational Learning with Causal Substructure"
  ☆24Updated 2 years ago
• yangnianzu0515 / MoleOOD
  Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022)…
  ☆61Updated 2 years ago
• SDS-Lab / QW_Loss
  A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)
  ☆10Updated last year
• KarolisMart / SPECTRE
  Reference implementation for SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators (ICML …
  ☆26Updated 2 years ago
• YangLing0818 / VQGraph
  [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
  ☆100Updated last year
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆200Updated 5 months ago
• Graph-COM / SPE
  Official code for SPE
  ☆17Updated 10 months ago
• LiamMa / GRIT
  This is an official implementation for "GRIT: Graph Inductive Biases in Transformers without Message Passing".
  ☆127Updated 7 months ago
• liugangcode / Data-Centric-Transfer
  [NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framewor…
  ☆21Updated 2 months ago
• tufts-ml / graph-generation-EDGE
  EDGE: Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
  ☆63Updated 2 months ago
• Namkyeong / CGIB
  The official source code for "Conditional Graph Information Bottleneck for Molecular Relational Learning".
  ☆42Updated 2 years ago
• tengxiao1 / GraphACL
  GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
  ☆31Updated last year
• Graph-and-Geometric-Learning / D4Explainer
  Official Implementation of "D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion"
  ☆23Updated last year
• Graph-COM / StruRW
  [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
  ☆21Updated 2 years ago
• IDEA-XL / InstructMol
  InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (COLING 2025)
  ☆47Updated 8 months ago
• HICAI-ZJU / iMoLD
  Official implementation for Learning Invariant Molecular Representation in Latent Discrete Space (NeurIPS 2023)
  ☆21Updated last year
• Data-reindeer / MolScaling
  Source code for the paper 'Uncovering Neural Scaling Laws in Molecular Representation Learning' (NeurIPS 2023 Datasets and Benchmarks).
  ☆14Updated last year
• qslim / PDF
  Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
  ☆13Updated last year
• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆44Updated 2 years ago
• shamim-hussain / egt_pytorch
  Edge-Augmented Graph Transformer
  ☆78Updated last year
• Graph-and-Geometric-Learning / MolGroup
  Learning to Group Auxiliary Datasets for Molecule, NeurIPS2023
  ☆19Updated last year
• mengliu1998 / DL4DisassortativeGraphs
  Papers about developing DL methods on disassortative graphs
  ☆48Updated 3 years ago