  The role of weight on the weighted networks is investigated by studying the
effect of weight on community structures. We use weighted modularity $Q^w$ to
evaluate the partitions and Weighted Extremal Optimization algorithm to detect
communities. Starting from idealized and empirical weighted networks, the
distribution or matching between weights and edges are disturbed. Using
dissimilarity function $D$ to distinguish the difference between community
structures, it is found that the redistribution of weights does strongly affect
the community structure especially in dense networks. This indicates that the
community structure in networks is a suitable property to reflect the role of
weight.
