graph neural networks

Resolving Kinase Resistance Landscapes via Antisymmetric Siamese Graph Networks and Co-Folding Ensembles

Acquired clinical resistance to small-molecule kinase inhibitors arising from single-point mutations within the ATP-binding cleft presents a critical challenge in precision oncology. Conventional computational modeling relies either on rigid docking—which overlooks induced structural alterations—or computationally dema...

Human-in-the-Loop Cultural Schema Annotation for Metaphor Translation: A Pilot Framework

Metaphor translation presents a persistent challenge in cross-lingual communication because source and target languages often organize metaphorical meaning through different cultural schemas. This pilot study proposes a human-in-the-loop annotation framework for tracing cultural schema transfer in Chinese-English metap...

Learning Circuit Structures for Intelligent Digital Logic Optimization

Abstract Digital logic systems become increasingly difficult to examine and improve as their structures grow in size and complexity. Traditional Electronic Design Automation (EDA) methods generally depend on established algorithms and rule-based procedures, which may become less convenient when many gates and signal co...

The Luna Bound Propagator for Formal Analysis of Neural Networks

Real-time optimization of urban traffic using predictive algorithms

Urban traffic congestion remains a critical challenge in modern cities, contributing to increased travel times, environmental pollution, and economic inefficiencies. This research paper explores the application of predictive algorithms for real-time optimization of urban traffic flow, aiming to enhance mobility and red...

CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI

Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise graphs, thereby obscuring condition-specific multi-ROI organization. We introduce CoHyFuse, a condition-aware ROI-centered hypergraph framework...

GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement

Reinforcement learning (RL) has emerged as a promising approach for placement optimization, particularly when combined with graph neural networks (GNNs) that capture circuit connectivity. However, most learning-based placement approaches focus on floorplanning, macro placement, or global placement, while detailed place...

Supply Chain Credit Risk Propagation Modeling Through Heterogeneous Graph Learning and Enterprise Relation Inference

Supply chain finance involves complex credit relationships among core enterprises, upstream suppliers, downstream distributors, logistics providers, and financial institutions. Traditional risk assessment models often rely on isolated financial indicators and fail to capture risk propagation caused by delayed payments,...

Upstream and Downstream Credit Exposure Modeling Around Core Industrial Enterprises

Core industrial enterprises often anchor large supplier and distributor networks, and their operational or financial changes may affect upstream procurement, downstream sales, trade credit, receivable turnover, and bank credit exposure. Credit risk assessment focused only on a single borrower may miss hidden risk accum...

Tax-Invoice Relation Modeling for Financial Distress Prediction in Small and Medium Industrial Enterprises

Small and medium industrial enterprises often lack complete public financial disclosures, making financial distress prediction difficult when relying only on balance-sheet indicators. Tax invoices, supplier-customer relations, sales concentration, purchase volatility, and payment behavior provide alternative signals fo...

Physics-Guided multi-scale graph sLSTM for long-term runoff forecasting

A mixed-methods matrix approach to explainable GNNs in migrant network capital

Examination of how an XAI–GNN model reinforced network capital (NC) among Laotian migrant workers in Bangkok, Thailand provided evidence supporting the effectiveness of XAI-driven network analysis using GNNs for identifying latent dimensions of migrant NC in host countries.