2008/09/26 by Mitrovic Marija, Marija, Mitrovic, Bosiljka Tadić +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Statistical Mechanics (cond-mat.stat-mech) #Topological and Geometric Data Analysis #cond-mat.stat-mech #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.0809.4610
arxiv created 2008/09/26 · openalex publication_date 2008/09/26 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Real-data networks often appear to have strong modularity, or network-of-networks structure, in which subgraphs of various size and consistency occur. Finding the respective subgraph structure is of great importance, in particular for understanding the dynamics on these networks. Here we study modular networks using generalized method of maximum likelihood. We first demonstrate how the method works on computer-generated networks with the subgraphs of controlled connection strengths and clustering. We then implement the algorithm which is based on weights of links and show its efficiency in finding weighted subgraphs on fully connected graph and on real-data network of yeast.