Results 21 to 30 of about 537,310 (265)
Graph Representation Ensemble Learning [PDF]
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020 Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links and classifying and recommending nodes. Most embedding methods aim to preserve specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult
Palash Goyal +7 more
openaire +1 more source
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 +7 more
doaj +1 more source
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
Graph Representation Learning Method Based on Neural Ranking with Embedded Hyperbolic Layer [PDF]
Jisuanji gongcheng, 2020 To address the high complexity of existing graph representation learning methods,this paper proposes a new graph representation learning method to improve the learning efficiency while maintaining the representation performance of graph features.The ...
TANG Suqin, LIU Xiaomei, YUAN Lei
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 +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
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 +4 more
doaj +1 more source