Not logged in.

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Link Prediction in Bipartite Nested Networks
Organization Unit
  • Matúš Medo
  • Manuel Mariani
  • Linyuan Lü
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Entropy
Publisher MDPI Publishing
Geographical Reach international
ISSN 1099-4300
Volume 20
Number 10
Page Range 777
Date 2018
Abstract Text 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
Free access at DOI
Digital Object Identifier 10.3390/e20100777
Other Identification Number merlin-id:16883
PDF File Download from ZORA
Export BibTeX