Showing 8 papers for 2026-07-16
This work introduces a topology-aligned inductive bias for molecular property prediction by mapping atoms to a fixed set of computational units and letting bonds govern which units share parameters. The bias is instantiated in two architectures: Iso-QGNN, a variational quantum circuit, and Iso-CGNN, a parameter-matched classical message-passing model. The goal is data-efficient learning in low-data quantum chemistry regimes, and the results demonstrate improved data efficiency and predictive performance by leveraging bond-topology alignment.
MxGPS proposes Multiplex Graph Transformers to counter topology overfitting in power-grid problems. Single-task GNN fine-tuning often performs well in-distribution but degrades under topology shift because gradient signals encode training-topology specifics. The multiplex graph transformer foundation model aims to learn physics-based relations across multiple grid topologies to generalize to unseen grids.
NodeImport tackles imbalanced node classification by assessing node importance to guide training, addressing minority-class underrepresentation. It moves beyond standard class balancing or synthetic resampling by introducing an importance-aware mechanism to reweight or select informative nodes. The approach yields improved minority-class performance on benchmark graphs.
This work improves molecular property prediction in small language models by augmenting prompts with graph-based tools at inference. A trained GNN expert provides predictive hints with confidence, and an instance-specific explanatory subgraph (subgraph SMILES plus explanation) is extracted to inform the LLM. The framework enables agentic tool use to inject graph-structural cues into zero-shot property prediction.
RF-Informed Graph Neural Networks offer accurate, data-efficient circuit performance prediction. The method uses topology-aware GNNs to capture the highly nonlinear RF circuit behavior with limited data, delivering accurate predictions across diverse topologies while reducing reliance on expensive simulations.
VMoGE (Variational Mixture of Graph Neural Experts) integrates multi-band EEG analysis with variational graph networks and a mixture-of-experts architecture for Alzheimer's disease recognition across frequency bands. By modeling frequency-specific patterns and uncertainty, it improves differential diagnosis between AD and other dementias and supports staging.
The authors develop an intention-driven temporal-graph learning framework to identify in-possession match phases in football from tracking data. They define three tactical intentions—Invade Opponent Space, Keep Possession, and Scoring—and six phases (including Build Up, Progression, Counter Attack, Maintenance, and two others) within a hierarchical model validated on seven Bundesliga matches.
This study analyzes how image encoder choice affects graph construction and classification in GCN-based breast ultrasound analysis. It systematically compares five image encoders to understand their impact on graph homophily, downstream classification performance, and practical guidance for encoder selection in ultrasound diagnosis.