Abstract
As the network security gradually deviates from the virtual environment to the real environment, the security problems caused by abnormal users in social networks are becoming increasingly prominent. These abnormal users usually form a group which can be regarded as an isolated network. This paper aims to detect the isolated maximal cliques from a dynamic social network for identifying the abnormal users in order to cut off the source of fake information in time. By virtue of concept stability, an isolated maximal clique detection approach is proposed. Experimental results shown that the proposed algorithm has a high F-measure value for detecting the isolated maximal cliques in social network.
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Acknowledgment
This work was funded in part by the National Natural Science Foundation of China (Grant No. 61702317), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2019JM-379), and the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Province (Grant No. 2017024). This is also a part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 840922. This work reflects only the authors’ view and the EU Commission is not responsible for any use that may be made of the information it contains.
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Gao, J., Hao, F., Yang, E., Yang, Y., Min, G. (2020). Concept Stability Based Isolated Maximal Cliques Detection in Dynamic Social Networks. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_11
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