  Community analysis is an important way to ascertain whether or not a complex
system consists of sub-structures with different properties. In this paper, we
give a two level community structure analysis for the SSCI journal system by
most similar co-citation pattern. Five different strategies for the selection
of most similar node (journal) pairs are introduced. The efficiency is checked
by the normalized mutual information technique. Statistical properties and
comparisons of the community results show that both of the two level detection
could give instructional information for the community structure of complex
systems. Further comparisons of the five strategies indicates that, the most
efficient strategy is to assign nodes with maximum similarity into the same
community whether the similarity information is complete or not, while random
selection generates small world local community with no inside order. These
results give valuable indication for efficient community detection by most
similar node pairs.
