Like clustering analysis, community detection aims at assigning nodes in anetwork into different communities. Fdp is a recently proposed density-basedclustering algorithm which does not need the number of clusters as prior inputand the result is insensitive to its parameter. However, Fdp cannot be directlyapplied to community detection due to its inability to recognize the communitycenters in the network. To solve the problem, a new community detection method(named IsoFdp) is proposed in this paper. First, we use Isomap technique to mapthe network data into a low dimensional manifold which can reveal diversepair-wised similarity. Then Fdp is applied to detect the communities innetworks. An improved partition density function is proposed to select theproper number of communities automatically. We test our method on bothsynthetic and real-world networks, and the results demonstrate theeffectiveness of our algorithm over the state-of-the-art methods.
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