Results 21 to 30 of about 533,476 (267)

Graph Representation Learning Based on Multi-Channel Graph Convolutional Autoencoders [PDF]

Jisuanji gongcheng, 2023
This study proposes a graph representation learning model based on multi-channel graph convolutional autoencoders to address the limited ability of graph convolutional autoencoders in fusing node attributes and graph topology, and their inability to ...
YUAN Lining, HU Hao, LIU Zhao
doaj   +1 more source

ATPGNN: Reconstruction of Neighborhood in Graph Neural Networks With Attention-Based Topological Patterns

IEEE Access, 2021
Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. However, some GNNs cannot make good use of positive information brought by nodes which are far away from each central node for ...
Kehao Wang, Hantao Qian, Xuming Zeng, Mozi Chen, Kezhong Liu, Kai Zheng, Pan Zhou, Dapeng Wu   +7 more
doaj   +1 more source

Learning Graph Augmentations to Learn Graph Representations

, 2022
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn ...
Hassani, Kaveh, Khasahmadi, Amir Hosein
openaire   +2 more sources

GAN‐based deep neural networks for graph representation learning

Engineering Reports, 2022
Graph representation learning has attracted increasing attention in a variety of applications that involve learning on non‐Euclidean data. Recently, generative adversarial networks(GAN) have been increasingly applied to the field of graph representation ...
Ming Zhao, Yinglong Zhang
doaj   +1 more source

Graph Signal Representation with Wasserstein Barycenters [PDF]

, 2018
In many applications signals reside on the vertices of weighted graphs. Thus, there is the need to learn low dimensional representations for graph signals that will allow for data analysis and interpretation.
Frossard, Pascal, Simou, Effrosyni
core   +2 more sources

GLAMOUR: Graph learning over macromolecule representations [PDF]

, 2021
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input.
Mohapatra, Somesh, An, Joyce, Gómez-Bombarelli, Rafael   +2 more
openaire   +2 more sources

Asymmetric Graph Representation Learning

, 2021
Despite the enormous success of graph neural networks (GNNs), most existing GNNs can only be applicable to undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed ...
Tan, Zhuo, Liu, Bin, Yin, Guosheng
openaire   +2 more sources

Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

Applied Sciences, 2023
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance.
Bader Alwasel, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti, Mohammed Alsuhaibani   +4 more
doaj   +1 more source

Local structure-aware graph contrastive representation learning

Neural Networks, 2023
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainly focus on the feature information of the global graph or small subgraph structure (e.g., the first-order neighborhood). In the paper,
Yang, Kai, Liu, Yuan, Zhao, Zijuan, Ding, Peijin, Zhao, Wenqian   +4 more
openaire   +3 more sources

A review on graph-based semi-supervised learning methods for hyperspectral image classification

Egyptian Journal of Remote Sensing and Space Sciences, 2020
In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
doaj   +1 more source