Strength prominence index: a link prediction method in fuzzy social network

Link prediction is a field within social network studies that aims to forecast future connections based on the structure of a social network. This paper introduces a link prediction method based on the strength and prominence of seed node pairs, referred to as the strength prominence index. In this method, we get a consistent score for any pair of nodes, regardless of whether they share a common neighbour. Several key characteristics have been identified. In our experiments, we used three well-known estimators to evaluate the accuracy of link prediction algorithms: precision, area under the precision-recall curve, and area under the receiver operating characteristic curve. A comparative study with existing methods is also presented, supported by relevant graphs and tables. Validation using Facebook data sets demonstrates the effectiveness of the proposed method.

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Publication Details

Journal
Complex & Intelligent Systems
Published
2025-05-29
DOI
https://doi.org/10.1007/s40747-025-01925-6
Citations
2
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.65
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article

Strength prominence index: a link prediction method in fuzzy social network

Antonios Kalampakas, Sakshi Dev Pandey, Leo Mršić, A. S. Ranadive et al.
2 citations
Complex & Intelligent Systems
Complex Network Analysis Techniques
2.65
article

Strength prominence index: a link prediction method in fuzzy social network

Antonios Kalampakas, Sakshi Dev Pandey, Leo Mršić, A. S. Ranadive, Sovan Samanta, Tofigh Allahviranloo
article en
2 citations

Abstract

Link prediction is a field within social network studies that aims to forecast future connections based on the structure of a social network. This paper introduces a link prediction method based on the strength and prominence of seed node pairs, referred to as the strength prominence index. In this method, we get a consistent score for any pair of nodes, regardless of whether they share a common neighbour. Several key characteristics have been identified. In our experiments, we used three well-known estimators to evaluate the accuracy of link prediction algorithms: precision, area under the precision-recall curve, and area under the receiver operating characteristic curve. A comparative study with existing methods is also presented, supported by relevant graphs and tables. Validation using Facebook data sets demonstrates the effectiveness of the proposed method.

Complex & Intelligent SystemsVol. 11(7)
Guru Ghasidas Vishwavidyalaya (IN), American University of the Middle East (KW), Higher Education Centre Novo Mesto (SI), Algebra Bernays University (HR), Istinye University (TR)
Openalex Percentile: Top 11%
Complex Network Analysis Techniques
2.65
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Strength prominence index: a link prediction method in fuzzy social network — Antonios Kalampakas, Sakshi Dev Pandey, et al. · Complex & Intelligent Systems (2025) | TGRS Research Map | TGRS