Abstract
Community detection is a fundamental research in network science, which has attracted researchers all over the world to devoting into this work. However, the existing algorithms can hardly hold performance and efficiency simultaneously. Aiming at addressing the problem, and inspired by the force in physics, this paper defines the node influence from a network perspective. Afterwards, a novel approach to detect communities in terms of influential nodes is proposed. Furthermore, the vital nodes and overlapping nodes can be obtained. Series of experiments on synthetic and real-world networks are conducted, and the experimental results show that the proposed algorithm is capable and effective, which provides a reliable solution for analyzing network structure in-depth.
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Acknowledgements
This work is partially supported by Liaoning Natural Science Foundation under Grant No. 20170540320, the Doctoral Scientific Research Foundation of Liaoning Province under Grant No. 20170520358, the National Natural Science Foundation of China under Grant No. 61473073, the Fundamental Research Funds for the Central Universities under Grant No. N161702001, No. N172410005-2.
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Huang, X., Chen, D., Ren, T., Wang, D. (2020). CDIA: A Feasible Community Detection Algorithm Based on Influential Nodes in Complex Networks. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_100
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DOI: https://doi.org/10.1007/978-3-030-32456-8_100
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