Path-based extensions of local link prediction methods for complex networks.

Aziz, Furqan, Gul, Haji, Uddin, Irfan and Gkoutos, Georgios V (2020) Path-based extensions of local link prediction methods for complex networks. Scientific reports, 10 (1). p. 19848. ISSN 2045-2322. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QA Mathematics. Computing
QZ Pathology. Oncology
W Public health. Health statistics. Occupational health. Health education
WU Dentistry. Oral surgery
Divisions: Clinical Support
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Depositing User: Jamie Edgar
Date Deposited: 23 Nov 2020 15:44
Last Modified: 23 Nov 2020 15:44
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3710

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