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GitHub topics: molecular-graph-learning

NSLab-CUK/S-CGIB

Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure PyTorch backend.

Language: Python - Size: 4.17 MB - Last synced at: 8 days ago - Pushed at: 9 days ago - Stars: 6 - Forks: 2

liugangcode/data_centric_transfer

[NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framework with diffusion model on graphs.

Language: Python - Size: 1.25 MB - Last synced at: 11 months ago - Pushed at: about 1 year ago - Stars: 14 - Forks: 2

shamim-hussain/egt_pytorch

Edge-Augmented Graph Transformer

Language: Python - Size: 79.1 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 67 - Forks: 9

gu-yaowen/CurrMG

An efficient curriculum learning-based strategy for molecular graph learning

Language: Python - Size: 1.26 MB - Last synced at: 11 months ago - Pushed at: over 1 year ago - Stars: 14 - Forks: 3

quantori/structure-seer

The implementation, training and evaluation of a Structure Seer machine learning model designed for reconstruction of adjacency of a molecular graph from the labelling of its nodes.

Language: Python - Size: 43.2 MB - Last synced at: 11 days ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1