Results 21 to 30 of about 3,653 (212)
Citation Recommendation via Hierarchical Attributed Network Representation Learning
Jisuanji kexue yu tansuo, 2021 Citation recommendation (CR) is able to intelligently generate a paper list related to a query paper, which is of great value to researches. CR problem is related to papers?? semantic and structural information. Recently, network representation learning (
CHEN Jie, LIU Yang, ZHAO Shu, ZHANG Yanping
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Sampling networks by nodal attributes [PDF]
Physical Review E, 2019 In a social network individuals or nodes connect to other nodes by choosing one of the channels of communication at a time to re-establish the existing social links. Since available data sets are usually restricted to a limited number of channels or layers, these autonomous decision making processes by the nodes constitute the sampling of a multiplex ...
Jo, Hang Hyun +5 more
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Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
Applied Sciences, 2021 As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity ...
Junjian Zhan +4 more
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Node Embedding Method Based on Folded Path Aggregation on Attributed Network [PDF]
Jisuanji gongcheng, 2023 Attributed network embedding is a challenging task in the field of graph analysis.It aims to learn the low-dimensional vector representation of nodes from the network topology and node attributes of the network while maintaining its structure and ...
Mingchang BAI
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Academic Collaborator Recommendation Based on Attributed Network Embedding
Journal of Data and Information Science, 2022 Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.
Du Ouxia, Li Ya
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Influential Attributed Communities via Graph Convolutional Network (InfACom-GCN)
Information, 2022 Community search is a basic problem in graph analysis. In many applications, network nodes have certain properties that are important for the community to make sense of the application; hence, attributes are associated with nodes to capture their ...
Nariman Adel Hussein +2 more
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Jointly Learning Representations of Nodes and Attributes for Attributed Networks [PDF]
ACM Transactions on Information Systems, 2020 Previous embedding methods for attributed networks aim at learning low-dimensional vector representations only for nodes but not for both nodes and attributes, resulting in the fact that node embeddings cannot be directly used to recover the correlations between nodes and attributes.
Zaiqiao Meng +4 more
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Inter-Intra Information Preserving Attributed Network Embedding
IEEE Access, 2019 To alleviate the problem caused by the sparsity of network structure which is often the case in large-scale network, attributed network embedding has attracted an increasing amount of attention. Some existing attributed network embedding models integrate
Kai Wang +5 more
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Hierarchical label with imbalance and attributed network structure fusion for network embedding
AI Open, 2022 Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure).
Shu Zhao +4 more
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Semantic Attribute Matching Networks [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations.
Seungryong Kim +5 more
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