Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Category

graph neural networks

1,828 papers

#graph neural networks Open access Sep 2026

Global trade network prediction based on spatiotemporal graph neural network and gradient boosting model

This paper addresses the challenges of nonlinearity, spatiotemporal dependence, and coupling with external factors in global trade network forecasting. A hybrid model (ST-GBDT) integrating spatiotemporal graph neural networks and gradient boosting decision trees is proposed. This model constructs a multi-channel spat...

Tian-Wen Zhao, Guo-Qing Chen, Li-Li Zhang et al. · 0 citations
#graph neural networks Sep 2026

Benchmarking Experiments with External Data, Domain-Specific Features, and Learning Strategies for Modeling ADME and Potency of Coronavirus Main Protease Inhibitors

This work tested the key molecular descriptors capable of identifying antivirals, augmenting challenge data sets with curated public data, and building both classical machine learning and graph neural network (GNN) variants, providing both practical benchmarked modeling strategies for ADME and potency prediction and a...

Ida Titus, Ashok Palaniappan · 0 citations
#graph neural networks Dataset Open access Sep 2026

Anomalous Patterns in Quantum Constraint Systems and Large-Scale Structure: Observations Inviting Further Investigation

Abstract During computational experiments on quantum evolution in organized topological systems, we observed several anomalous patterns that we are unable to fully explain within current physical frameworks. Specifically: (1) Physical topology explains only ~35-40% of how systems respond to constraint, leaving a substa...

Bernhard Bonaventura Edward Reck · 0 citations
#graph neural networks Open access Sep 2026

Biomaterials Explained: Materials, Implants & Biomedical Engineering Applications

This academic curriculum module delivers an analytical, biophysical, and computational exposition of biomaterials science, tissue engineering scaffolds, biocompatibility evaluation, degradation kinetics, and medical device materials engineering. Key Technical Topics & Curricular Areas Covered:1. Core Material Classes &...

Prep4Uni.Online · 0 citations
#graph neural networks Open access Sep 2026

Enhancing protein–ligand pose prediction via dual-branch deep learning with docking-oriented multi-component loss and iterative refinement

Protein-ligand pose prediction is a core task in structure-based drug discovery because it determines how a ligand fits within a protein pocket and directly affects downstream virtual screening and lead-optimization workflows. Recent graph neural network (GNN) methods have shown promise for protein-ligand pose predicti...

Md. Khorshed Alam, Julia Rahman, M. A. Hakim Newton et al. · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Ray-Traced D2D Link Scheduling: Dataset, Code and Results for "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels"

Supporting dataset, source code, trained models and result files for the manuscript "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels" (Sensors, MDPI; revised version). The archive contains 24,000 device-to-device network snapshots generated...

Tae-Won Ban · 0 citations
#reinforcement learning Open access Sep 2026

LEARNING-BASED OPTIMIZATION OF DIGITAL HARDWARE: AI METHODS FOR SYNTHESIS, PHYSICAL DESIGN, AND VERIFICATION

Abstract The increasing complexity of digital systems has made hardware optimization a learning problem as much as an algorithmic one. Conventional electronic design automation (EDA) flows depend on manually engineered heuristics that may not generalize across architectures, workloads, and technology nodes. This journa...

Gem Galangue · 0 citations
#reinforcement learning Open access Sep 2026

LEARNING-BASED OPTIMIZATION OF DIGITAL HARDWARE: AI METHODS FOR SYNTHESIS, PHYSICAL DESIGN, AND VERIFICATION

Abstract The increasing complexity of digital systems has made hardware optimization a learning problem as much as an algorithmic one. Conventional electronic design automation (EDA) flows depend on manually engineered heuristics that may not generalize across architectures, workloads, and technology nodes. This journa...

Gem Galangue · 0 citations
#reinforcement learning Open access Sep 2026

AI-Based Optimization of ALU Power Consumption in Modern Computer Architectures

To address the power efficiency bottlenecks and thermal design limits inherent in modern sub-micron microprocessors, this paper introduces an AI-driven optimization framework designed to minimize dynamic and static power dissipation in multi-bit Arithmetic Logic Units (ALUs) without sacrificing operating frequency or t...

Gail Rizaga · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Ray-Traced D2D Link Scheduling: Dataset, Code and Results for "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels"

Supporting dataset, source code, trained models and result files for the manuscript "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels" (Sensors, MDPI; revised version). The archive contains 24,000 device-to-device network snapshots generated...

Tae-Won Ban · 0 citations
#graph neural networks Open access Sep 2026

FedTrust-GNN: Federated Learning with Blockchain Trust and Graph Neural Networks for Decentralized Modeling

Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...

Sourish Dey -, Anish Pandey, Shreyanjan Neogi et al. · 0 citations
#graph neural networks Open access Sep 2026

FedTrust-GNN: Federated Learning with Blockchain Trust and Graph Neural Networks for Decentralized Modeling

Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...

Sourish Dey -, Anish Pandey, Shreyanjan Neogi et al. · 0 citations

From tech blogs

See all →
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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.