Abstract
Aiming at the problems of existing algorithms, such as instability, neglecting interaction between nodes and neglecting attributes of node, an overlapping community discovery algorithm combining topological potential and trust value of nodes was proposed. Firstly, the importance of nodes is calculated according to topological potential and the trust value of the node, and then K core nodes are selected. In final, the final division of communities are finished by using the extended modularity and core nodes. Experimental results on LFR network datasets and three real network datasets, verify the efficiency of the proposed OCDTT algorithm.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (No. 61762078, 61862058, 61967013), Youth Teacher Scientific Capability Promoting Project of NWNU (No. NWNU-LKQN-16-20).
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Li, X., Kong, W., Wei, W., Fu, E., Ma, H. (2020). Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes. In: Shi, Z., Vadera, S., Chang, E. (eds) Intelligent Information Processing X. IIP 2020. IFIP Advances in Information and Communication Technology, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-030-46931-3_15
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DOI: https://doi.org/10.1007/978-3-030-46931-3_15
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