Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network
Strong structural controllability (SSC) guarantees networked system with linear-invariant
dynamics controllable for all numerical realizations of parameters. Current research has …
dynamics controllable for all numerical realizations of parameters. Current research has …
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Y Jin, M Daoutis, S Girdzijauskas… - 2022 International Joint …, 2022 - ieeexplore.ieee.org
Accurate routing network status estimation is a key component in Software Defined
Networking. However, existing deep-learning-based methods for modeling network routing …
Networking. However, existing deep-learning-based methods for modeling network routing …
DGLP: Incorporating Orientation Information for Enhanced Link Prediction in Directed Graphs
Link prediction in directed graphs offers a solution for uncovering detailed and accurate
relationships among distinct entities. Unlike conventional link prediction in undirected …
relationships among distinct entities. Unlike conventional link prediction in undirected …
Anomalous Link Detection in Dynamically Evolving Scale-Free-Like Networked Systems
Networked systems are becoming increasingly complex and interconnected, making them
more vulnerable to attacks. Identifying anomalous communication links is of paramount …
more vulnerable to attacks. Identifying anomalous communication links is of paramount …
Graph Neural Networks and Time Series as Directed Graphs for Quality Recognition
A Simonetti, F Zanchetta - arXiv preprint arXiv:2310.02774, 2023 - arxiv.org
Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled
with existing algorithms as Temporal Convolutional Networks and Recurrent Neural …
with existing algorithms as Temporal Convolutional Networks and Recurrent Neural …
TEDGCN: Asymmetric Spatiotemporal GNN for Heterogeneous Traffic Prediction
Y Ku, Y Wang, Q Liu, Y Yang… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Mutual influence among different transport activities is a crucial factor for heterogeneous
traffic prediction in modern urban transport systems. The asymmetry of influence has been …
traffic prediction in modern urban transport systems. The asymmetry of influence has been …
Graph modelled system change detection in WSNs
R Zakrzewski, T Martin… - 2022 11th Mediterranean …, 2022 - ieeexplore.ieee.org
Graphs are suitable to model topology and data patterns in systems such as WSNs. To
detect change, there is a need for graph comparison, a computationally demanding task …
detect change, there is a need for graph comparison, a computationally demanding task …
Multi-scale Directed Graph Convolution Neural Network for Node Classification Task
F Li, D Xu, F Liu, Y Meng, X Liu - International Conference on Neural …, 2023 - Springer
The existence of problems and objects in the real world which can be naturally modeled by
complex graph structure has motivated researchers to combine deep learning techniques …
complex graph structure has motivated researchers to combine deep learning techniques …
Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review
P Ayesha Barvin, T Sampradeepraj - Engineering Proceedings, 2023 - mdpi.com
Data-driven approaches and resource management to improve yield are becoming
increasingly frequent in agriculture with the progress in technology. Based on a broad …
increasingly frequent in agriculture with the progress in technology. Based on a broad …
Graph Neural Networks with Feature and Structure Aware Random Walk
Graph Neural Networks (GNNs) have received increasing attention for representation
learning in various machine learning tasks. However, most existing GNNs applying …
learning in various machine learning tasks. However, most existing GNNs applying …