基于大规模复杂网络社区发现的科研合著网络分析

In this paper, we design a parallel computing framework using MapReduce and present a scientific co-authorship network analytical algorithm by detecting commutities in large-scale complex network with reference to the community detection algorithm based on maximum clique. Then we use the proposed algorithm to detect the community structure of domestic scientific co-authorship network in the field of management science and engineering in 2012. Experimental results show that the parallel community detection algorithm based on MapReduce can not only ensure the correctness but also improve the efficiency of the computation. Thus, this algorithm can be used to detect community structure in large-scale complex network. The community structure detection results of domestic scientific co-authorship network in the field of management science and engineering in 2012 illustrate the features and problems of domestic scientific collaboration in the field.