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Showing 1 to 4 of 4 for “"Stochastic Blockmodel"”.

  1. Statistical algorithms using multisets and statistical inference of heterogeneous networks

    … heterogeneous version of the mixed membership stochastic blockmodel. We also apply a variational algorithm for posterior inference. We demonstrate the advantage of the proposed method, in modeling overlapping communities and multiple memberships, through simulation studies and applications to …

    uiuc Repository record for Statistical algorithms using multisets and statistical inference of heterogeneous networks (opens in a new tab)

  2. Statistical analysis of networks with community structure and bootstrap methods for big data

    … community detection literature. We propose a blockmodel for heterogeneous networks with community structure, and introduce a heterogeneous spectral clustering algorithm for community detection in heterogeneous networks. Theoretical properties of the clustering algorithm under the proposed …

    uiuc Repository record for Statistical analysis of networks with community structure and bootstrap methods for big data (opens in a new tab)

  3. Statistical inference for complex networks

    … datasets. Heterogeneous mixed-membership stochastic blockmodel (MMSB) is powerful for modeling overlapping communities and multiple community memberships, but many methods in the literature lack a theoretical guarantee. In the second project, we study the theoretical properties of the …

    uiuc Repository record for Statistical inference for complex networks (opens in a new tab)

  4. Modeling and projection of respondent driven network samples

    … the Exponential Random Graph Model (ERGM) and a Stochastic Block Model (SBM) to an empirical study of the Facebook friendship network of 1034 participants. Then, given our goal of identifying techniques that will produce a representative sample, we decided to compare two version of AC-RDSs, in …

    ksu Repository record for Modeling and projection of respondent driven network samples (opens in a new tab)