  Identifying clusters or community structures in networks has become an
integral part of social network analysis. Though many methods were proposed,
the label propagation algorithm (LPA) is a popular computationally efficient
method with running time linear. However, the LPA provides different
combination of communities on the same network due to the randomness in LPA.
Many improvements have been proposed to tackle this stability problem by
eliminating the randomness. This paper put forward an improvement to the
standard LPA by proposing a label initialization method based on link
similarity. The similarity is measured based on the connection strength between
two nodes. The method is tested on real and synthetic measures to analyze the
performance.
