Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Detection of Maximal Balance Clique Using Three-way Concept Lattice

  • Yixuan Yang (Dept. of Software Convergence, Soonchunhyang University) ;
  • Doo-Soon Park (Dept. of Software Convergence, Soonchunhyang University) ;
  • Fei Hao (School of Computer Science, Shaanxi Normal University) ;
  • Sony Peng (Dept. of Software Convergence, Soonchunhyang University) ;
  • Hyejung Lee (Dept. of Innovation and Convergence, Hoseo University) ;
  • Min-Pyo Hong (Dept. of Computer Sciences and Engineering, Soonchunhyang University)
  • Received : 2021.11.22
  • Accepted : 2022.10.01
  • Published : 2023.04.30
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Abstract

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.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (No. NRF-2020RIA2B5B01002134) and the BK21 Fostering Outstanding Universities for Research (No. 5199990914048), European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 840922, the National Natural Science Foundation of China (No. 61702317), and the Fundamental Research Funds for the Central Universities (No. GK202103080).

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