graph neural networks

Hybrid Federated Learning Framework for APT Attack Chain Detection and Privacy Enhancement

This paper introduces a time-series graph neural network detection model to achieve accurate perception and correlation analysis of multi-stage behaviors of APT attacks and constructs a two-layer federated learning framework of horizontal cross-organization collaboration and vertical multi-feature fusion.

Closed-Loop Decision-Focused Learning for User-Aware Cloud Orchestration under Uncertainty

Heterogeneous job scheduling is formulated as a multi-objective combinatorial optimization problem (MOCOP) under uncertain constraints and a closed-loop decision-focused learning (CL-DFL) framework for cloud orchestration is proposed to improve robustness under heterogeneous workloads.

Learning to solve the minimum dominating set problem via graph neural networks and hybrid decoding

Optimal Allocation Model for Social Capital Resources in Community Health Management Based on Multi-Agent Deep Reinforcement Learning

The proposed dynamic graph representation and multi-agent optimization strategy provides a computational framework for distributed information coordination and adaptive resource scheduling in intelligent electromagnetic sensing and communication environments, where reliable network interaction and efficient information...

TabSOM: A tabular-to-image encoding method based on self-organizing maps

TabSOM is proposed, a tabular-to-image encoding built on the Self-Organizing Map, which provides a spatial layout in which every input feature occupies a fixed canvas position derived from its component plane via collision-free Hungarian assignment and a graph that captures pairwise feature relationships derived from t...

Topology-Aware Graph Neural Network with Spatiotemporal Encoding and Contrastive Learning for Scalable Dynamic Network Representation

The implications of the proposed TAGNN framework on such applications as anomaly detection, social media analytics, and traffic forecasting are that the proposed framework provides an end-to-end solution that promotes accuracy, efficiency, and temporal consistency in dynamic graph learning.

HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks

Hierarchical Interpolating MeshGraphNets (HI-MGN), a multiscale extension of MeshGraphNets for efficient long-range communication on unstructured meshes that replaces the flat processor with a hierarchical multiscale processor that coarsens graphs using farthest-point sampling and Voronoi partitioning while preserving...

Modeling of Fault Propagation Paths in Power Communication Networks Combined with GNN

A method combining graph neural networks (GNNs) to construct a propagation path modeling framework that integrates topological structure and node status time series characteristics and is directly applicable to reliability analysis of electromagnetic communication links in power networks.

Dependence-Informed Sparse Neural Architecture for Stock Return Prediction

Two structural ablations indicate that both the sparse connectivity and the estimated grouping of characteristics contribute to the ranking advantage, and both effects remain significant after correcting for multiple testing.

Understanding critical-class prioritization in class integration test order generation with a GNN-D3QN framework

Investigation of when and why critical-class-first scheduling is effective in CITO and a GNN-D3QN joint framework is proposed to unify critical class identification and test order optimization suggests that the effectiveness of critical-class prioritization is structurally conditioned by the topological roles and distr...

Self-Supervised Graph Vision Transformer Network For Early Pulmonary Nodule Detection And Malignancy Classification From Ct Imaging

A Self-Supervised Graph Vision Transformer Network (SS-GVTNet) in detecting and classifying pulmonary nodules and malignancies through CT scanning and offers credible classification of malignancy, which help clinicians in early diagnosis and treatment planning is suggested.

Development and optimization of multidimensional data fusion and intelligent decision system for university students' employment market

An intelligent employment decision support system that integrates multi-source heterogeneous data with GNNs (Graph Neural Networks), which can effectively identify structurally mismatched groups, provide quantitative basis for professional adjustment and regional talent policies, and demonstrate good technical performa...