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  <doc>
    <id>5116</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2014-08-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modularity of Directed Networks: Cycle Decomposition Approach</title>
    <abstract language="eng">The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number of fuzzy clustering methods for directed networks, many of these approaches tend to neglect important directional information. In this paper, we present a novel random walk based approach for finding fuzzy partitions of directed, weighted networks, where edge directions play a crucial role in defining how well nodes in a module are interconnected. We will show that cycle decomposition of a random walk process connects the notion of network modules and information transport in a network, leading to a new, symmetric measure of node communication. Finally, we will use this measure to introduce a communication graph, for which we will show that although being undirected it inherits all necessary information about modular structures from the original network.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-51166</identifier>
    <identifier type="doi">10.3934/jcd.2015.2.1</identifier>
    <enrichment key="SourceTitle">Appeared in: Journal of Computational Dynamics 2 (2015) pp. 1-24</enrichment>
    <author>Natasa Djurdjevac Conrad</author>
    <submitter>Erlinda Körnig</submitter>
    <author>Ralf Banisch</author>
    <author>Christof Schütte</author>
    <series>
      <title>ZIB-Report</title>
      <number>14-31</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>directed networks, modules, cycle decomposition, measure of node communication</value>
    </subject>
    <collection role="msc" number="05C81">Random walks on graphs</collection>
    <collection role="msc" number="60J20">Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) [See also 90B30, 91D10, 91D35, 91E40]</collection>
    <collection role="msc" number="94C15">Applications of graph theory [See also 05Cxx, 68R10]</collection>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="projects" number="NonequiMSM">NonequiMSM</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/5116/ZIB-Report_14-31.pdf</file>
  </doc>
</export-example>
