1 research outputs found
Community Detection in Complex Networks Using Density-based Clustering Algorithm
Like clustering analysis, community detection aims at assigning nodes in a
network into different communities. Fdp is a recently proposed density-based
clustering algorithm which does not need the number of clusters as prior input
and the result is insensitive to its parameter. However, Fdp cannot be directly
applied to community detection due to its inability to recognize the community
centers 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 map
the network data into a low dimensional manifold which can reveal diverse
pair-wised similarity. Then Fdp is applied to detect the communities in
networks. An improved partition density function is proposed to select the
proper number of communities automatically. We test our method on both
synthetic and real-world networks, and the results demonstrate the
effectiveness of our algorithm over the state-of-the-art methods