  A significant problem in analysis of complex network is to reveal community
structure, in which network nodes are tightly connected in the same
communities, between which there are sparse connections. Previous algorithms
for community detection in real-world networks have the shortcomings of high
complexity or requiring for prior information such as the number or sizes of
communities or are unable to obtain the same resulting partition in multiple
runs. In this paper, we proposed a simple and effective algorithm that uses the
correlation of nodes alone, which requires neither optimization of predefined
objective function nor information about the number or sizes of communities. We
test our algorithm on real-world and synthetic graphs whose community structure
is already known and observe that the proposed algorithm detects this known
structure with high applicability and reliability. We also apply the algorithm
to some networks whose community structure is unknown and find that it detects
deterministic and informative community partitions in these cases.
