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Enhancing network embedding with implicit clustering

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conference contribution
posted on 2023-05-23, 14:06 authored by Li, Q, Zhong, J, Cao, Z, Wang, C
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.


Publication title

Proceedings of the 24th International Conference, DASFAA 2019: Database Systems for Advanced Applications


G Li, J Gama, Y Tong, J Yang, J Natwichai






School of Information and Communication Technology


Springer Nature Switzerland

Place of publication


Event title

24th International Conference, DASFAA 2019: Database Systems for Advanced Applications

Event Venue

Chiang Mai, Thailand

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright © 2019 Springer Nature Switzerland AG

Repository Status

  • Restricted

Socio-economic Objectives

Intelligence, surveillance and space

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    University Of Tasmania