Abstract
Discovering close contacts and the key structures in social networks plays a vital role in network analysis. The existing methods for identifying key network structures often suffer from high computational complexity, and lack a clear and reasonable semantic explanation. To tackle this issue, we propose a method for close contact detection by using the technic of formal concept analysis. Specifically, we establish the relationship between social networks and formal contexts, and adopt possible attribute analysis to discover close contacts and identify prime cliques. After that, we discuss the dynamic updating mechanism of close contacts and prime cliques under the evolution of a social network. In addition, we conduct some experiments to show the relationships between the number of prime cliques and the size of social networks, and the feasibility and effectiveness of the proposed updating methods.
Supported by the Natural Science Foundation of Henan Province under Grant 222300420445, and the Fundamental Research Funds for the Universities of Henan Province under Grant NSFRF210318.
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Acknowledgements
We would like to thank the organization committee of the 2022 International Joint Conference on Rough Sets, which provides us an opportunity to share recent developments in conceptual knowledge discovery and machine learning based on three-way decisions and granular computing.
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Zhi, H., Li, J., Qi, J. (2022). Close Contact Detection in Social Networks via Possible Attribute Analysis. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_23
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