Abstract
This paper proposes a novel algorithm based on local similarities to detect community structure in complex network. By analyzing the strengths and weaknesses of popular similarity indexes, a new index of node similarity is defined which can reflect closeness of local connections in networks as community does. And the similarity between a node and a community is defined by the sum of similarities between this node and all nodes within the community. Then networks can be partitioned without presetting the number of communities based on the assumption that nodes with highest similarities tend to merge together, additionally bridging nodes as byproducts. This method's effectiveness is confirmed by applying it to the IEEE 39-bus and 118-bus standard power grids. Influence of the bridging nodes in cascading failures is also discussed.
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