Detection of Maximal Balance Clique Using Three-way Concept Lattice

In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential...

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Bibliographic Details
Published in:Journal of information processing systems 2023-04, Vol.19 (2), p.189-202
Main Authors: Yixuan Yang, Doo-Soon Park, Fei Hao, Sony Peng, Hyejung Lee, Min-Pyo Hong
Format: Article
Language:Korean
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Summary:In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.
ISSN:1976-913X
2092-805X