Results 11 to 20 of about 574,210 (309)
Multi-View Network Representation Learning Algorithm Research
Algorithms, 2019 Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional ...
Zhonglin Ye +3 more
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Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network
Journal of Social Computing, 2021 Network representation learning algorithms, which aim at automatically encoding graphs into low-dimensional vector representations with a variety of node similarity definitions, have a wide range of downstream applications.
Weiwei Gu +3 more
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Network representation learning based on social similarities
Frontiers in Environmental Science, 2022 Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In
Ziwei Mo +5 more
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An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
Applied Sciences, 2022 Community structure is a network characteristic where nodes can be naturally divided into densely connected groups. Community structures are ubiquitous in social, biological, and technological networks.
Yinan Chen, Chuanpeng Wang, Dong Li
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Role-Based Network Representation Learning Method [PDF]
Jisuanji gongcheng, 2021 Network representation learning is widely used to obtain the characteristics and semantics of network nodes. The existing network representation learning methods mainly study the adjacency matrix or the power of the adjacency matrix,making a node in the ...
XU You, WANG Xiaoping, XIONG Yun
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Cross-domain network representations [PDF]
Pattern Recognition, 2019 The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in network representation by employing random walks as the network sampling strategy.
Shan Xue 0001 +2 more
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Object Discovery and Representation Networks
, 2022 European Conference on Computer Vision (ECCV ...
Olivier J. Hénaff +7 more
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Universal network representation for heterogeneous information networks [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes
Ruiqi Hu +5 more
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Heterogeneous Network Representation Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations.
Yuxiao Dong +4 more
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Road Network Topology-aware Trajectory Representation Learning [PDF]
Jisuanji kexue, 2023 The approaches developed for task trajectory representation learning(TRL) on road networks can be divided into the following two categories,i.e.,recurrent neural network(RNN) and long short-term memory (LSTM) based sequence models,and the self-attention ...
CHEN Jiajun, CHEN Wei, ZHAO Lei
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