Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm.

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ID: 90248
2020
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Abstract
Real world complex networks are indirect representation of complex systems. They grow over time. These networks are fragmented and raucous in practice. An important concern about complex network is link prediction. Link prediction aims to determine the possibility of probable edges. The link prediction demand is often spotted in social networks for recommending new friends, and, in recommender systems for recommending new items (movies, gadgets etc) based on earlier shopping history. In this work, we propose a new link prediction algorithm namely "Common Neighbor and Centrality based Parameterized Algorithm" (CCPA) to suggest the formation of new links in complex networks. Using AUC (Area Under the receiver operating characteristic Curve) as evaluation criterion, we perform an extensive experimental evaluation of our proposed algorithm on eight real world data sets, and against eight benchmark algorithms. The results validate the improved performance of our proposed algorithm.
Reference Key
ahmad2020missingscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ahmad, Iftikhar;Akhtar, Muhammad Usman;Noor, Salma;Shahnaz, Ambreen;
Journal Scientific reports
Year 2020
DOI
10.1038/s41598-019-57304-y
URL
Keywords
ion exchange chromatography, aminoacidopathies, inherited metabolic disorders, organic aciduria.

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