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Review

Review on Learning and Extracting Graph Features for Link Prediction

by 1,†, 2,†, 2,† and 1,2,*
1
Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
2
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Mach. Learn. Knowl. Extr. 2020, 2(4), 672-704; https://doi.org/10.3390/make2040036
Received: 29 October 2020 / Revised: 6 December 2020 / Accepted: 11 December 2020 / Published: 17 December 2020
Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions. View Full-Text
Keywords: complex networks; graph analysis; proximity; supervised link prediction; unsupervised link prediction complex networks; graph analysis; proximity; supervised link prediction; unsupervised link prediction
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MDPI and ACS Style

Mutlu, E.C.; Oghaz, T.; Rajabi, A.; Garibay, I. Review on Learning and Extracting Graph Features for Link Prediction. Mach. Learn. Knowl. Extr. 2020, 2, 672-704. https://doi.org/10.3390/make2040036

AMA Style

Mutlu EC, Oghaz T, Rajabi A, Garibay I. Review on Learning and Extracting Graph Features for Link Prediction. Machine Learning and Knowledge Extraction. 2020; 2(4):672-704. https://doi.org/10.3390/make2040036

Chicago/Turabian Style

Mutlu, Ece C., Toktam Oghaz, Amirarsalan Rajabi, and Ivan Garibay. 2020. "Review on Learning and Extracting Graph Features for Link Prediction" Machine Learning and Knowledge Extraction 2, no. 4: 672-704. https://doi.org/10.3390/make2040036

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