Community detection in complex networks using density-based clustering algorithm and manifold learning

• A density-based clustering framework is proposed for community structure detection.
• An improved partition density is proposed to evaluate the quality of the detected communities.
• The framework is insensitive to its parameters, and easy to implement.
• The comparisons performed on the synthetic benchmarks and the real-world networks show the effectiveness of the framework.

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 the network. 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.