title: Efficient community detection of network flows for varying Markov times and bipartite networks.
abstract: Community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.
authors.affiliation: Department of IT and Computer Engineering, Iran University of Science and Technology, Teheran, Iran.|Integrated Science Lab, Department of Physics, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Kheirkhahzadeh
authors.affiliation: Integrated Science Lab, Department of Physics, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Andrea
authors.initials: A
authors.lastname: Lancichinetti
authors.affiliation: Integrated Science Lab, Department of Physics, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Martin
authors.initials: M
authors.lastname: Rosvall