graph neural networks

Data Center Thermal Optimization Using Graph Neural Networks

Data center cooling accounts for a significant fraction of facility energy consumption, motivating the development of intelligent control strategies that improve efficiency while maintaining thermal safety. This study presents a graph-based digital twin for data center thermal optimization that enables safe online rein...

Editorial: Neurobiological foundations of cognition and progress toward artificial general intelligence

The problems are as follows: a) what is intelligence, b) how human intelligence differs from that in the other species, c) how could intelligence emerge in the physical universe, d) what is the future of AI? e) The last decade has witnessed breakthrough advances in machine learning methods rooted in the ideas of digita...

DRL-Enabled Polymorphic Acceleration Framework for Flexible and Energy-Efficient Hybrid-Float Deep Learning Inference in Mobile Computing

Hybrid-float quantization has emerged as a promising solution for efficient deep neural network inference on mobile platforms, but its practical deployment is still limited by three challenges: compute–transmission imbalance, rapidly growing mapping complexity, and the tradeoff between intermediate-data movement and ha...

Invariant Subgraph Learning via Effective Environment Subgraph Augmentation for Generalizable Graph Neural Network

With the increasing prevalence of graph data in various practical applications, Graph Neural Networks (GNNs) have established themselves as essential tools for effective graph data processing. However, existing GNNs always perform well on in-distribution data, but exhibit significant performance degradation under distr...

Cross-City POI Recommendation Combining Intention-Aware and Review Sentiment Analysis

Cross-city Point of interest (POI) recommendation aims to recommend locations for users who move from a source city to a target city. Most existing cross-city POI recommendation methods consider users’ check-in records from their source city to analyze user preferences, while neglecting the crucial information in the r...

Fed-MoSeC: A Federated Learning Framework for Cross-Modal Semantic Communication Systems in Mobile Networks

Semantic communication systems in mobile networks necessitate real-time collaborative updates of semantic encoders as a result of node mobility that induces semantic extraction drift. However, diversity within modal transmission content and heterogeneous encoder architectures, along with challenges such as imbalanced t...

HADES: Heterogeneity-Aware Dependent Task Offloading in Collaborative Edge Networks

Regarding the computational intensity and stringent latency requirements of modern applications comprised of many dependent subtasks, collaborative edge computing (CEC) emerges as a solution to guarantee the service-level objectives (SLOs) by offloading subtasks to multiple distributed edge servers. However, existing o...

DetO-RAN: Dual-Timescale Resource Allocation for Flows in Deterministic Open RAN

Deterministic transmission and computation are essential for open radio access networks (O-RANs) to support latency-critical and computation-intensive applications. However, existing O-RAN and time-sensitive networking integrations mainly focus on deterministic guarantees in wired fronthaul transmission, while lacking...

GEM-Distill: Graph Ensemble Multi-Level Distillation for MLP-Based Graph Classification

Graph Neural Networks (GNNs) achieve strong performance on graph learning tasks, but their message-passing computation hinders deployment in resource-constrained settings. GNN-to-MLP (G2M) knowledge distillation improves inference efficiency by transferring GNN knowledge to lightweight MLP students; however, existing m...

Graph Pyramid Pooling Transformer for Graph-Level Representation Learning

On surrogate modeling of static response of AM short-fiber thermoplastics using graph neural networks

Do Not Search Blindly: A Multi-agent Graph-Aware Search for Graph Neural Networks