Results 21 to 30 of about 78,912 (260)
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
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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
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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
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Dual Graph Representation Learning
CoRR, 2020 Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs.
Huiling Zhu, Xin Luo, Hankz Hankui Zhuo
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Learning Graph Representations With Maximal Cliques
IEEE Transactions on Neural Networks and Learning Systems, 2023 Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance.
Molaei, S +4 more
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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
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Learning Graph Representations by Dendrograms
CoRR, 2018 Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal representation of the graph defines a class
Thomas Bonald, Bertrand Charpentier
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Representation Learning for Spatial Graphs
CoRR, 2018 4 pages, 1 figure ...
Zheng Wang 0046 +3 more
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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
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Graph Representation Ensemble Learning
CoRR, 2019 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 certain properties of the original graph in the low dimensional space.
Palash Goyal +5 more
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