\/svg>","ionicons-filled--link":"<\/svg>"}) Accessibility Tools Invert colors Monochrome Dark contrast Light contrast Low saturation High saturation Highlight links Highlight headings Screen reader Read mode Content scaling 100% Font size 100% Line height 100% Letter spacing 100% Skip to main content PL The Institute The Institute General information Emploees News Scientific News Gender equality plan Address and contact data Research Research profile List of publications Information in BIP Scientific Council Organizational structure GDPR Events Seminars Current seminars List of seminars Conferences Current conferences Past conferences For students Doctoral school General Information Curriculum Recruitment School Council Doctoral Student Council Teaching Doctoral students Mid-term evaluation For students Master theses Student training Visiting the Institute For employees Institute e-mail Eduroam Publication registry Contact us Address and contact data Important phone numbers and emails PL The Institute The Institute General information Emploees News Scientific News Gender equality plan Address and contact data Research Research profile List of publications Information in BIP Scientific Council Organizational structure GDPR Events Seminars Current seminars List of seminars Conferences Current conferences Past conferences For students Doctoral school General Information Curriculum Recruitment School Council Doctoral Student Council Teaching Doctoral students Mid-term evaluation For students Master theses Student training Visiting the Institute For employees Institute e-mail Eduroam Publication registry Contact us Address and contact data Important phone numbers and emails Events Home Events List of seminars Seminar "Coherence-Correlations-Complexity", Dept. of Theoretical Physics, Wrocław University of Technology 13:15, 15-06-17 Sala 320a bud. A-1, Politechnika Wrocławska Measuring the level of hierarchy in complex networksdr Daniel CzegelDepartment of Biological Physics, Eötvös Loránd University, BudapestSigns of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.
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Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.