Network-Embedding-Based Link Prediction Using the Expectation Maximization Algorithm 


Vol. 44,  No. 11, pp. 2123-2126, Nov.  2019
10.7840/kics.2019.44.11.2123


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  Abstract

Recently, network embedding methods employing deep learning have been introduced in the field of network science, and are shown to perform more effectively than conventional methods in the sense of solving downstream machine learning tasks such as node classification, link prediction, and so forth. In this letter, we propose a method that applies the expectation maximization (EM) algorithm to network embedding to improve the accuracy of link prediction. The superiority of the proposed method is shown in terms of AUC.

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  Cite this article

[IEEE Style]

G. Park, C. Tran, W. Shin, "Network-Embedding-Based Link Prediction Using the Expectation Maximization Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 11, pp. 2123-2126, 2019. DOI: 10.7840/kics.2019.44.11.2123.

[ACM Style]

Gyeong-Taek Park, Cong Tran, and Won-Yong Shin. 2019. Network-Embedding-Based Link Prediction Using the Expectation Maximization Algorithm. The Journal of Korean Institute of Communications and Information Sciences, 44, 11, (2019), 2123-2126. DOI: 10.7840/kics.2019.44.11.2123.

[KICS Style]

Gyeong-Taek Park, Cong Tran, Won-Yong Shin, "Network-Embedding-Based Link Prediction Using the Expectation Maximization Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 11, pp. 2123-2126, 11. 2019. (https://doi.org/10.7840/kics.2019.44.11.2123)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880