graph neural networks

Vision transformer and graph neural networks based SuryaNet for yoga pose quality assessment

SuryaNet is introduced, a multi-task model with four technical contributions that establishes a new state-of-the-art in terms of quality assessment, while it also provides users with quality feedback.

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 learning, process-aware relational modeling, and anomaly scoring. In the first stage, global and local frequency features are extracted through fast Fourier transform (FFT)-based analysis and target attention mechanisms, which are then incorporated into a conditional variational autoencoders (VAEs) to learn frequency-enhanced latent representations. In the second stage, sensors are grouped according to production process stages, and intraprocess bidirectional gated recurrent unit (GRU) networks capture sequential dependencies among sensors; the learned embeddings guide adaptive graph structure learning, and graph attention mechanisms aggregate neighbor information for multistep prediction. In the third stage, reconstruction and prediction errors are fused through a weighted harmonic mean to compute robust anomaly scores. The proposed method has three main advantages: 1) the frequency-enhanced modules effectively process nonstationary industrial time series by extracting both global spectral patterns and local time-varying frequency characteristics; 2) the process-aware sensor embedding leverages domain knowledge to guide graph structure learning, constructing sensor correlation graphs that conform to physical process flows; and 3) the joint optimization of reconstruction and prediction tasks enables the model to learn representations that capture both data distribution patterns and temporal dynamics, improving detection coverage for various anomaly types. Comprehensive experiments on the proprietary cigarette-production dataset cigarette industrial process time-series data (CIG) and two public benchmarks [pooled server metrics (PSM) and mars science laboratory (MSL)] compared FCVAE-GNN against seven representative baselines. On the public PSM benchmark, FCVAE-GNN achieves the best F1 score of 0.6104 and AUC-ROC of 0.8040, and across the three datasets, it attains the highest mean AUC-ROC of 0.6932, demonstrating consistent cross-dataset stability suitable for industrial deployment.

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 the optimal switching control strategy. In this paper, a novel Graph Neural Network Reinforcement Learning (GNN-RL), a machine learning based optimal switching controller is applied for APFs in the renewable integrated grid. The GNN’s spatial relationship modelling is used to integrate the complex network configurations of power distribution network and adaptive control is achieved under dynamic operating conditions using RL optimization. The proposed work is carried out in a modified IEEE 13 bus distribution feeder with the integration of PV and Wind sources. The results show that the notable reduction in Total Harmonic Distortion (THD) as compared to the traditional PI controlled based switching in different loading conditions

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 bus network topology, and improving model generalization. To address these issues, this paper proposes a graph transfer learning-based approach for bus passenger flow prediction. First, a dynamic graph neural network is used to construct the passenger flow network, and Framelet Transform along with Self-Expressiveness Regularization is applied for denoising, enhancing data quality. Second, an adaptive neighborhood-aware dynamic graph convolutional network is introduced, integrating random mask enhancement, hop count perception fusion, and multi-channel spatiotemporal feature extraction for accurate passenger flow modeling. Furthermore, a combination of source data and source free transfer learning is leveraged to optimize feature distribution alignment through pseudo-label generation, graph diffusion, and consistency loss. Finally, a GRU is used for passenger flow prediction, and extensive experiments on real-world datasets test the proposed method. Results demonstrate superior accuracy and generalization performance compared to existing state of the art (SOTA) models.

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 information. Is that really the case? Simply explaining this by saying that the cortex has many neurons, numerous neural connections, and complex electrochemical activity between them is not sufficient to answer these fundamental questions. We need to uncover the theory hidden behind these phenomena. In essence, a neural network is equivalent to a very large directed graph, with a massive number of nodes and directed connections. This paper posits that the physical form of long-term memory is a connected subgraph within this complex directed graph. This subgraph is capable of linking together the disparate fragments of the same event, spread across different sensory cortices, to form associations. This provides a physical realization of the engram theory. The robustness of the connected subgraph and the resources it consumes can explain various memory behaviors. Based on anatomical, brain imaging, and electrophysiological evidence, this paper constructs a probabilistic connectivity model and uses theorems from graph theory to prove the ease of constructing connected subgraphs. Finally, it explains why the potential capacity for memory is immense.

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 propagation are critical.

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 the SOM component planes.