A Max-Cut-specific graph neural network predicts primal- and dual-feasible SDP solutions in linearithmic time, cutting bounding costs in exact branch-and-bound by up to 10.6 times versus a commercial SDP solver while training without any solved SDP labels.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.
HANNA is a thermodynamically consistent ML model for predicting excess Gibbs energy from molecular structures, trained on various binary mixture data and extended to multi-component mixtures using geometric projection.
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.
citing papers explorer
-
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
A Max-Cut-specific graph neural network predicts primal- and dual-feasible SDP solutions in linearithmic time, cutting bounding costs in exact branch-and-bound by up to 10.6 times versus a commercial SDP solver while training without any solved SDP labels.
-
MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.
-
Thermodynamically consistent machine learning model for excess Gibbs energy
HANNA is a thermodynamically consistent ML model for predicting excess Gibbs energy from molecular structures, trained on various binary mixture data and extended to multi-component mixtures using geometric projection.
-
Spatial statistics for screening molecular structures
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.