Title: Community discovery method based on complex network of data fusion based on super network perspective

Authors: Li Pei

Addresses: School of Computer Science & Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi Sheng, China; Shaanxi Key Laboratory of Network Data Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi Sheng, China

Abstract: To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the Minimum Description Length (MDL) of information compression is proposed in this paper. Firstly, the community detection is converted to information compression problem of seeking effective network structure and the quality evaluation function is constructed based on MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. At last, the simulation experiment in the standard test set and API capture Sina Microblog data set shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision.

Keywords: super network perspective; label movement; complex network; heuristic; community discovery.

DOI: 10.1504/IJCAT.2019.102094

International Journal of Computer Applications in Technology, 2019 Vol.61 No.1/2, pp.54 - 61

Received: 25 Aug 2018
Accepted: 19 Oct 2018

Published online: 06 Sep 2019 *

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