Abstract
In this work we investigate the applicability of binary similarity and distance measures in the context of Link Prediction. Neighbourhood-based similarity measures to assess the similarity of nodes in a network have been long available. They boast the main advantage of low calculation complexity, because only a local view of the network is required. Neighbourhood-based measures are used in a variety of Link Prediction applications, including bioinformatics, bibliographic networks and recommender systems. It is possible to use binary measures in the same context, retaining the same prerogatives and possibly increasing the link prediction performances in domain-specific tasks. Preliminary studies have also been conducted on widely-accepted data sets.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 |
| Editors | Wei Li, Qingli Li, Lipo Wang |
| Publisher | IEEE |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538676042, 9781538676035 |
| ISBN (Print) | 9781538676059 |
| DOIs | |
| Publication status | Published - 13 Oct 2018 |
| Event | 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 - Beijing, China Duration: 13 Oct 2018 → 15 Oct 2018 |
Publication series
| Name | Proceedings - International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI |
|---|
Conference
| Conference | 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 13/10/18 → 15/10/18 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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