# Graph Convolutional Network

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bioRxiv Nov 5, 2026
Tumor subtyping moves beyond broad categories: HHRM classifies eight hierarchy levels and reaches 0.7924 accuracy on pan-cancer test data
Tumor subtypes form a hierarchy running from primary cancer type down to molecular subtype, yet most classifiers flatten the labels and ignore the biological constraints between levels. HHRM combines cancer hallmark gene networks with recursive modules to predict level by level across 11,476 pan-cancer samples covering 26 primary cancers, 405 subtypes and up to eight levels, reaching 0.7924 accuracy in local output mode, above the hierarchical models it was compared with. Retrained on an independent set of 1,608 breast cancer samples, it exceeded 0.86 accuracy on both test and validation data.
Tumor subtypes form a hierarchy running from primary cancer type down to molecular subtype, yet most classifiers flatten the labels and ignore the biological constraints between levels. HHRM combines cancer hallmark gene networks with recursive modules to predict level by level across 11,476 pan-cancer samples covering 26 primary cancers, 405 subtypes and up to eight levels, reaching 0.7924 accuracy in local output mode, above the hierarchical models it was compared with. Retrained on an independent set of 1,608 breast cancer samples, it exceeded 0.86 accuracy on both test and validation data.
Tampere University South China University of Technology
Qing Song（First author）Xin Lai（Corresponding author）
HHRM Hierarchical Multi-Label Classification Graph Convolutional Network SHAP
arXiv Nov 9, 2026
Multi-step reaction mechanisms move beyond single-step templates: graph-guided preference data lifts GRMP pathway accuracy to 48.1%
Multi-step reaction mechanism prediction has largely stayed at the level of single elementary steps and generalizes poorly to unseen reaction spaces. GRMP couples a chemical mechanism knowledge graph, completed with expert templates, to a language model: a graph convolutional network generates candidate pathways that become preference data for direct preference optimization, and at inference a relation retrieval module pulls graph evidence to refine the pathway. GRMP reaches pathway accuracy of 48.1% on ChemMechQA and 57.9% on oMe-Gold, the highest among the compared methods.
Multi-step reaction mechanism prediction has largely stayed at the level of single elementary steps and generalizes poorly to unseen reaction spaces. GRMP couples a chemical mechanism knowledge graph, completed with expert templates, to a language model: a graph convolutional network generates candidate pathways that become preference data for direct preference optimization, and at inference a relation retrieval module pulls graph evidence to refine the pathway. GRMP reaches pathway accuracy of 48.1% on ChemMechQA and 57.9% on oMe-Gold, the highest among the compared methods.
Yu Zhang（First author）
GRMP ChemMechKG Multi-step chemical mechanism prediction Direct Preference Optimization Graph Convolutional Network
bioRxiv (Cold Spring Harbor Laboratory) Oct 24, 2026
Memorability models flip their scores under noise, while GraphMem tracks human judgments by reading how images relate
After progressive Gaussian noise was added to LaMem images, mainstream memorability models such as ResMem, ViTMem and AMNet estimated lower memorability for initially memorable images and higher memorability for initially forgettable ones, a reversal resembling stochastic resonance. In a continuous recognition task with 88 participants, memorable images proved more resistant to noise while forgettable images declined sharply. The authors built GraphMem, which estimates memorability from an image's neighborhood in a similarity graph rather than from single-image features; it aligned most closely with human data under noise and blur, and after noise training its slope effect reached ρ=0.57 (95% bootstrap CI [0.29, 0.75]), the only model to turn positive.
After progressive Gaussian noise was added to LaMem images, mainstream memorability models such as ResMem, ViTMem and AMNet estimated lower memorability for initially memorable images and higher memorability for initially forgettable ones, a reversal resembling stochastic resonance. In a continuous recognition task with 88 participants, memorable images proved more resistant to noise while forgettable images declined sharply. The authors built GraphMem, which estimates memorability from an image's neighborhood in a similarity graph rather than from single-image features; it aligned most closely with human data under noise and blur, and after noise training its slope effect reached ρ=0.57 (95% bootstrap CI [0.29, 0.75]), the only model to turn positive.
George Mason University
Huiyu Ding（First author）Martin Wiener（Corresponding author）
GraphMem Image Memorability Graph Convolutional Network Stochastic Resonance

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Tumor subtyping moves beyond broad categories: HHRM classifies eight hierarchy levels and reaches 0.7924 accuracy on pan-cancer test data

Multi-step reaction mechanisms move beyond single-step templates: graph-guided preference data lifts GRMP pathway accuracy to 48.1%

Memorability models flip their scores under noise, while GraphMem tracks human judgments by reading how images relate