Article: X-SSAS: human-machine interaction-driven framework for explainable and scalable similarity-based link prediction in social networks Journal: International Journal of Computational Science and Engineering (IJCSE) 2026 Vol.29 No.4 pp.384 - 407 Abstract: This study proposes a novel unified similarity-based link prediction method, X-SSAS, that addresses the limitations posed by traditional methods and serves as a medium to explore human-machine interaction. X-SSAS combines both structural similarity and attribute similarity controlled by a tuning parameter to generate efficient similarity scores that considers both network topology and node attributes. The proposed study provides a novel approach to k-medoid clustering using X-SSAS scores and offers transparent predictions using explainable artificial intelligence. Extensive experiments are conducted on 14 real-world datasets and X-SSAS is tested using six well-known evaluation metrics. The results demonstrate that X-SSAS outperforms existing studies and four widely used similarity measures in their weighted version. The proposed X-SSAS framework attains 99.86% accuracy and 99.99% AUROC on the <i>C. elegans</i> datasets. X-SSAS provides enhanced accuracy, transparency, trustworthiness, and scalability across diverse real-world network datasets. Inderscience Publishers - linking academia, business and industry through research

Title: X-SSAS: human-machine interaction-driven framework for explainable and scalable similarity-based link prediction in social networks

Authors: Mridula Dwivedi; Vipin Saxena; Babita Pandey

Addresses: Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, 226025, India ' Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, 226025, India ' Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, 226025, India

Abstract: This study proposes a novel unified similarity-based link prediction method, X-SSAS, that addresses the limitations posed by traditional methods and serves as a medium to explore human-machine interaction. X-SSAS combines both structural similarity and attribute similarity controlled by a tuning parameter to generate efficient similarity scores that considers both network topology and node attributes. The proposed study provides a novel approach to k-medoid clustering using X-SSAS scores and offers transparent predictions using explainable artificial intelligence. Extensive experiments are conducted on 14 real-world datasets and X-SSAS is tested using six well-known evaluation metrics. The results demonstrate that X-SSAS outperforms existing studies and four widely used similarity measures in their weighted version. The proposed X-SSAS framework attains 99.86% accuracy and 99.99% AUROC on the C. elegans datasets. X-SSAS provides enhanced accuracy, transparency, trustworthiness, and scalability across diverse real-world network datasets.

Keywords: link prediction; explainable artificial intelligence; XAI; k-medoid clustering; HMI; social network; structural similarity; attribute similarity; Jaccard's coefficient; Euclidean distance.

DOI: 10.1504/IJCSE.2026.155097

International Journal of Computational Science and Engineering, 2026 Vol.29 No.4, pp.384 - 407

Received: 19 Dec 2025
Accepted: 28 Jan 2026
Published online: 27 Jul 2026 *

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Title: X-SSAS: human-machine interaction-driven framework for explainable and scalable similarity-based link prediction in social networks

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