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Graph representation learning with encoding edges

journal contribution
posted on 2023-05-20, 06:06 authored by Li, Q, Cao, Z, Zhong, J
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.

History

Publication title

Neurocomputing

Volume

361

Pagination

29-39

ISSN

0925-2312

Department/School

School of Information and Communication Technology

Publisher

Elsevier Science Bv

Place of publication

Po Box 211, Amsterdam, Netherlands, 1000 Ae

Rights statement

©2019 Elsevier B.V. All rights reserved.

Repository Status

  • Restricted

Socio-economic Objectives

Intelligence, surveillance and space

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