[Objective] This paper compares community detection algorithms in the field of complex network analysis, aiming to support related information science studies. [Methods] First, we identified the similarities and differences of several community detection algorithms (i.e. theoretical frameworks and calculation methods). Then, we examined these algorithms with small data sets. Third, we expanded the sample size, and evaluated the performance of Louvain algorithm, Louvain algorithm with multilevel refinement, and the SLM algorithm with the collaboration and citation networks. [Results] On small dataset, the detection results of GN and FN algorithms were similar, and the results of SLM algorithm were better than those of the Louvain algorithm and Louvain algorithm with multilevel refinement. In the field of library and information science, setting the resolution at 0.5 could help us analyze the detection results. The results of SLM algorithm were different to those of the Louvain algorithm or Louvain algorithm with multilevel refinement. Results of the latter two were almost the same, which were different with the resolution of 1.0. [Limitations] The dataset needs to be expanded. [Conclusions] The Louvain algorithm, Louvain algorithm with multilevel refinement and SLM algorithm are better than traditional algorithms. Among them, the SLM algorithm is the best option for us to analyze the community of citation network.
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Chen Yunwei,Zhang Ruihong. Comparing on Community Detection Algorithms for Information Mining. Data Analysis and Knowledge Discovery, 2018, 2(10): 84-94.
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