The efficiency of community detection by most similar node pairs

Community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. Avoiding the shortages of computation complexity and pre-given assumption, in this paper, we give a two level community structure analysis for the SSCI journal system by most similar node pairs. Five different strategies for the selection of node pairs are introduced. The efficiency is checked by normalized mutual information technique. Statistical properties and comparisons of the community results show that both of the two level detection could give instructional information of the community structure of complex systems. Further comparisons of the five strategies indicates that, it is always efficient to assign nodes with maximum similarity into the same community whether the similarity information is complete or not, while rational random selection with too much information and random selection generate small world local community with no inside order. These results give valuable indication for efficient community detection by most similar node pairs.