graph neural networks

TcfDyG: Cryptocurrency Network Anomaly Detection Based on Time-aware Channel Fusion Dynamic Graph Neural Network

Process-Aware Frequency-Enhanced Conditional VAE With Graph Neural Networks for Anomaly Detection of Industrial Multivariate Time Series

This article presents a novel anomaly detection method for industrial multivariate time series (IMTS) based on a process-aware frequency-enhanced conditional variational autoencoder (FCVAE) and graph neural networks (GNN). The proposed FCVAE-GNN method consists of three main stages: frequency-enhanced representation le...

GNN-RL APF Switching Controller for Power Quality Enhancement in Renewable Integrated Grids

The integration of renewable or green energy sources in the grids introduce several problems like power quality issues such as harmonic distortion, voltage fluctuations and unbalanced reactive power flow etc. Active Power Filters (APF) can effectively work on these issues. However, the key performance of APFs depend on...

A Graph-Based Transfer Learning Approach for Short-Term Bus Passenger Flow Prediction

Bus passenger flow prediction is a crucial task in bus transportation management and optimization, characterized by strong spatiotemporal dependencies and influenced by external factors such as weather and holidays. However, existing methods face challenges in responding to sudden events, modeling dynamic changes in bu...

Identifying urban water spatial patterns and their underlying mechanism through graph neural network

A feasible probability and graph model for memory engrams

The capacity of long-term memory seems to be extremely large, capable of storing information spanning almost a lifetime. Why does it have such a vast capacity? Why are some memories so enduring? What is the actual physical form of long-term memory? In the movie Inside Out, it is depicted as individual orbs containing i...

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.