Next Article in Journal
Unconventional Deformation Behaviours of Nanoscaled High-Entropy Alloys
Next Article in Special Issue
A New and Stable Estimation Method of Country Economic Fitness and Product Complexity
Previous Article in Journal / Special Issue
Green Technology Fitness
Article Menu

Article Versions

Export Article

Open AccessArticle
Entropy 2018, 20(10), 777; https://doi.org/10.3390/e20100777

Link Prediction in Bipartite Nested Networks

1
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
3
Department of Physics, University of Fribourg, 1700 Fribourg, Switzerland
4
URPP Social Networks, Universität Zürich, 8050 Zürich, Switzerland
5
Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
*
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 8 October 2018 / Accepted: 8 October 2018 / Published: 10 October 2018
(This article belongs to the Special Issue Economic Fitness and Complexity)
PDF [372 KB, uploaded 10 October 2018]

Abstract

Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
Keywords: link prediction; nested networks; bipartite networks link prediction; nested networks; bipartite networks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Medo, M.; Mariani, M.S.; Lü, L. Link Prediction in Bipartite Nested Networks. Entropy 2018, 20, 777.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top