@CONFERENCE\{IMM2010-05964,
    author       = "M. M{\o}rup and M. N. Schmidt and L. K. Hansen",
    title        = "Infinite Multiple Membership Relational Modeling for Complex Networks",
    year         = "2010",
    booktitle    = "Presented first time at {NIPS} Workshop on Networks Across Disciplines in Theory and Application, published {IEEE} {MLSP} 2011",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5964-full.html",
    abstract     = "Learning latent structure in complex networks has become an   important problem fueled by many types of networked data originating   from practically all fields of science. In this paper, we propose a  new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection
  between the single membership relational model and multiple membership models and show on ``real{''} size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks."
}