2009/07/02 by Xiaofeng Gong, Gong, Xiaofeng, Shuguang Guan +4
Chemistry · Computer Science · Physics and Astronomy · #Blind Source Separation Techniques #Complex Network Analysis Techniques #Data Analysis #FOS: Physical sciences #Molecular spectroscopy and chirality #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an) #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.0907.0279
arxiv created 2009/07/02 · openalex publication_date 2009/07/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a multi-phase approach to explore network structures. In this method, structure analysis is not carried out on the observed network directly. Instead, certain similarity measures of the nodes are derived from the network firstly, which are then projected onto an appropriate lower-dimensional feature space. The clustering structure can be defined in the feature space, and analyzed by conventional clustering algorithms. The classified data are finally mapped back to the original network space if necessary to complete the analysis of network structures. By mapping onto the feature space, some difficulties due to the diversity of micro-structures and scale of the network can be circumvented. This makes it possible for the proposed method to deal with more general structures such as detecting groups in a random background, as well as identifying usual community structures in networks.