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graph neural networks

1,798 papers

#graph neural networks Dataset Open access Oct 2026

Data, trained models and code for "STEPHY: A Graph Neural Inference Framework for Rapid Estimation of Regional Epidemic Dynamics from Large Viral Phylogenies"

Data, trained models and code behind STEPHY: A Graph Neural Inference Framework for Rapid Estimation of Regional Epidemic Dynamics from Large Viral Phylogenies (https://doi.org/10.21203/rs.3.rs-10631496/v2). This deposit holds what the paper's results are made of: the simulation benchmark, the trained models, the Danis...

Leke Lyu · 0 citations
#graph neural networks Open access Oct 2026

Graph Neural Network-Based Analysis for Digital Logic Circuit Optimization

Abstract Digital logic circuits are becoming increasingly complex, making efficient circuit optimization an important part of Electronic Design Automation (EDA). Traditional circuit optimization methods may require extensive computation and predefined rules when dealing with complex circuit structures. This study aims...

ROBERT STEVEIN RECTO · 0 citations
#graph neural networks Oct 2026

Physics-Informed Neural Networks as Differentiable Surrogates for 4D-Variational Data Assimilation

Biogeochemical forecasting requires state estimation methods that accommodate sparse observations, nonlinear dynamics, and discontinuous ecological processes. Four-dimensional variational data assimilation (4D-Var) addresses this challenge but depends on tangent-linear and adjoint models that are costly to derive and m...

Kevin Joseph Egan, Brian Powell · 0 citations
#graph neural networks Dataset Open access Oct 2026

IMOS-SWIL District Heating Dataset

This dataset provides synchronized, high-resolution multi-physics measurements collected by IMOS-EPFL in collaboration with Aalborg University using the controlled district heating testbed at the Smart Water Infrastructures Laboratory (SWIL), Aalborg University, Denmark. The data were collected as part of the Intellige...

Keivan Faghih Niresi, Christian Møller Jensen, Carsten Skovmose Kallesøe et al. · 0 citations
#graph neural networks Open access Oct 2026

Intelligent Digital Logic Circuit Optimization Through Graph Neural Networks

Abstract The increasing complexity of digital logic circuits creates a need for effective methods of circuit analysis and optimization. Conventional Electronic Design Automation (EDA) techniques commonly depend on established algorithms and predefined rules, which can become difficult to apply to more complicated circu...

Starleo Madula · 0 citations
#graph neural networks Open access Oct 2026

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...

Ramer Agbuya · 0 citations
#graph neural networks Open access Oct 2026

KG-TransomicNet: Semantic–Quantitative Property Graph of PheKnowLator and Multi-Omics

KG-TransomicNet Version 1.1.2 · Software · Open · MIT De Filippis, Giovanni Maria · Rinaldi, Antonio Maria A semantic–quantitative property-graph framework that couples the PheKnowLator biomedical knowledge graph with per-sample multi-omics measurements in ArangoDB, enabling trans-omic analysis and knowledge-based reas...

Giovanni Maria De Filippis, Antonio M. Rinaldi · 0 citations
#graph neural networks Open access Oct 2026

Structure-behavior integrated risk prediction of vehicle groups using Graph Neural Networks.

Accurate identification of vehicle-group-level traffic risk is important for intelligent transportation safety management. Existing risk-prediction studies have mainly focused on individual vehicles, pairwise interactions, or aggregated surrogate safety indicators, while the role of group-level structure-behavior coupl...

Jing Gan, Yao Wu, Da-Peng Zhang et al. · 0 citations
#graph neural networks Open access Oct 2026

A Novel Mathematical Framework for Multi-Scale Neural Dynamics: Integrating Stochastic Differential Equations with Graph Neural Networks

The integration of multi-scale neural dynamics remains a fundamental challenge in computational neuroscience. This paper introduces a novel mathematical framework that integrates stochastic differential equations with graph neural networks to model multi-scale brain dynamics. Our approach treats neural activity, synapt...

Md Taufiq Nasseef · 0 citations
#graph neural networks Dataset Open access Oct 2026

Data, trained models and code for "STEPHY: A Graph Neural Inference Framework for Rapid Estimation of Regional Epidemic Dynamics from Large Viral Phylogenies"

Data, trained models and code behind STEPHY: A Graph Neural Inference Framework for Rapid Estimation of Regional Epidemic Dynamics from Large Viral Phylogenies (https://doi.org/10.21203/rs.3.rs-10631496/v2). This deposit holds what the paper's results are made of: the simulation benchmark, the trained models, the Danis...

Leke Lyu · 0 citations
#graph neural networks Open access Oct 2026

Exploring the key genes of colorectal “adenoma-cancer” based on graph transformer

Colorectal cancer (CRC) is one of the most lethal malignancies worldwide, and the precise identification of biomarkers from colonic adenoma to cancer is of great significance for preventing the development of adenocarcinoma. Given that existing methods inadequately capture the topological network relationships among ge...

Yue Yuan, Yu Liu, Jingchun Fan et al. · 0 citations

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Microsoft Research Blog Jul 13, 2026

Verifying Rust cryptography in SymCrypt, from standards to code

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

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