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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Link Prediction in Bipartite Nested Networks |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| 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 |
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