[en] Temporal attributed network embedding aspires to learn a low-dimensional vector representation for each node in each snapshot of a temporal network, which can be capable of various network analysis tasks such as link prediction and node classification. In temporal attributed networks, attribute similarities or link structures of certain nodes may deviate from the regular nodes of the community they belong to, which are called community outlier nodes. However, many existing embedding methods consider only the link structures and their attributes of the nodes adhere to the community structure of the network while ignoring outlier nodes, this can affect the embedding performance of the regular nodes. In this paper, we propose a temporal attributed network embedding framework with outliers, based on autoencoders, to solve the problem. In particular, we propose an outlier-aware autoencoder to model the node information, which combines the current snapshot and previous snapshots to jointly learn embedded vectors of nodes in the current snapshot of a temporal network. In feature preprocessing, we propose a simplified higher graph convolutional mechanism to incorporate attribute information into link structure information, which can leverage attribute features into link structure. Experimental results on node classification and link prediction reveal that our model is competitive against various baseline models.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Mo, Xian ; School of Information Engineering, Ningxia University, Yinchuan, China ; College of Computer & Information Science, Southwest University, Chongqing, China
PANG, Jun ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Liu, Zhiming ; College of Computer & Information Science, Southwest University, Chongqing, China
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Deep autoencoder architecture with outliers for temporal attributed network embedding
Date de publication/diffusion :
15 avril 2024
Titre du périodique :
Expert Systems with Applications
ISSN :
0957-4174
eISSN :
1873-6793
Maison d'édition :
Elsevier Ltd
Volume/Tome :
240
Pagination :
122596
Peer reviewed :
Peer reviewed vérifié par ORBi
Subventionnement (détails) :
The work is financed by the National Natural Science Foundation of China ( 62306157 , 62202320 ), 62032019 , Capacity Development Grant of Southwest University ( SWU116007 ).
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