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Enhancing network embedding with implicit clustering

Citation

Li, Q and Zhong, J and Li, Q and Cao, Z and Wang, C, Enhancing network embedding with implicit clustering, Proceedings of the 24th International Conference, DASFAA 2019: Database Systems for Advanced Applications, 22-25 April 2019, Chiang Mai, Thailand, pp. 452-467. ISSN 0302-9743 (2019) [Refereed Conference Paper]


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Copyright 2019 Springer Nature Switzerland AG

Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real- world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.

Item Details

Item Type:Refereed Conference Paper
Keywords:text mining, network embedding, feature learning, edge representation, network mining
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:132869
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2019-06-17
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