Graph neural networks in recommender systems: a survey
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …
alleviate such information overload. Due to the important application value of recommender …
How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
Nodeaug: Semi-supervised node classification with data augmentation
By using Data Augmentation (DA), we present a new method to enhance Graph
Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised …
Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised …
Alg: Fast and accurate active learning framework for graph convolutional networks
Graph Convolutional Networks (GCNs) have become state-of-the-art methods in many
supervised and semi-supervised graph representation learning scenarios. In order to …
supervised and semi-supervised graph representation learning scenarios. In order to …
Fractal graph convolutional network with MLP-mixer based multi-path feature fusion for classification of histopathological images
The spatial information among different tissue components and multi-level features is
important in histopathological images for pathologists to diagnose cancers. Graph …
important in histopathological images for pathologists to diagnose cancers. Graph …
A survey of Computationally Efficient graph neural networks for reconfigurable systems
Graph neural networks (GNNs) are powerful models capable of managing intricate
connections in non-Euclidean data, such as social networks, physical systems, chemical …
connections in non-Euclidean data, such as social networks, physical systems, chemical …
Hyper-GST: predict metro passenger flow incorporating graphSAGE, hypergraph, social-meaningful edge weights and temporal exploitation
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning.
Deep learning algorithms have been widely applied due to their robust performance in …
Deep learning algorithms have been widely applied due to their robust performance in …
Detecting implementation bugs in graph convolutional network based node classifiers
Graph convolutional networks (GCNs) have achieved state-of-the-art performance on the
task of node classification. However, the performance of GCNs is prone to implementation …
task of node classification. However, the performance of GCNs is prone to implementation …
EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision
Edge vision systems combining sensing and embedded processing promise low-latency,
decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to …
decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to …
Machine learning driven exploration of energies, and Generalization of topological indices for the fuzzy conjugate graph of dihedral group
MU Mirza, R Anjum, AF Alrasheedi, J Kim - IEEE Access, 2024 - ieeexplore.ieee.org
This study introduces a ground-breaking approach to analyzing dihedral groups through the
lens of fuzzy graph theory, significantly enhancing computational efficiency in group theory …
lens of fuzzy graph theory, significantly enhancing computational efficiency in group theory …