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January 1, 2026International Journal of Computational Science and Engineering

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

Mridula Dwivedi Vipin Saxena Babita Pandey

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Key Points

This work aims to establish a scalable and explainable framework for link prediction in social networks using human-machine interaction.
Developed a human-machine interaction model for link prediction.
Utilized similarity-based measures to enhance predictive capability.
Tested the model in various social network scenarios.
Demonstrated improved prediction accuracy over traditional methods.
Increased user comprehension of prediction rationale through explainable outputs.

Implication

Explores a human-machine interaction framework for link prediction in social networks, highlighting its scalability and explanation capabilities.

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Cite This Study

Dwivedi et al. (2026) studied this question.

synapsesocial.com/papers/69f19ff5edf4b4682480697d https://doi.org/10.1504/ijcse.2026.10077984

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