Community structure is an important feature to understand structural and functional properties in various complex networks. In this paper, we use Multidimensional Scaling (MDS) to map nodes of network into Euclidean space to keep the distance information of nodes, and then we use topology feature of communities to propose the local expansion strategy to detect initial seeds for FCM. Finally, the FCM are used to uncover overlapping communities in the complex networks. The test results in real-world and artificial networks show that the proposed algorithm is efficient and robust in uncovering overlapping community structure.

Cooperative spectrum sensing technology has been identified as an effective approach to mitigate the interference generated by cognitive radio users on primary networks. In this paper, based on the rigorous mathematical derivation, we proposed an interference model of cooperative cognitive radio networks on the licensed user over Rayleigh fading wireless channel. In the case of the cognitive radio users independently and cooperatively sensing the existence of the licensed user, the proposed model analyzes the effect of some key parameters on the interference such as the missing detection probability of spectrum sensing scheme, spatial density of cognitive radio users etc. This model not only is a theoretical expression but also can be put into applications. The numerical results are given to verify the proposed model.