Weighted network motifs as random walk patterns
Over the last two decades, network theory has shown to be a fruitful paradigm in understanding the organization and functioning of real-world complex systems. One technique helpful to this endeavor is identifying functionally influential subgraphs, shedding light on underlying evolutionary processes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac6f75 |
| _version_ | 1797748529171529728 |
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| author | Francesco Picciolo Franco Ruzzenenti Petter Holme Rossana Mastrandrea |
| author_facet | Francesco Picciolo Franco Ruzzenenti Petter Holme Rossana Mastrandrea |
| author_sort | Francesco Picciolo |
| collection | DOAJ |
| description | Over the last two decades, network theory has shown to be a fruitful paradigm in understanding the organization and functioning of real-world complex systems. One technique helpful to this endeavor is identifying functionally influential subgraphs, shedding light on underlying evolutionary processes. Such overrepresented subgraphs, motifs , have received much attention in simple networks, where edges are either on or off. However, for weighted networks, motif analysis is still undeveloped. Here, we proposed a novel methodology—based on a random walker taking a fixed maximum number of steps—to study weighted motifs of limited size. We introduce a sink node to balance the network and allow the detection of configurations within an a priori fixed number of steps for the random walker. We applied this approach to different real networks and selected a specific null model based on maximum-entropy to test the significance of weighted motifs occurrence. We found that identified similarities enable the classifications of systems according to functioning mechanisms associated with specific configurations: economic networks exhibit close patterns while differentiating from ecological systems without any a priori assumption. |
| first_indexed | 2024-03-12T16:07:06Z |
| format | Article |
| id | doaj.art-ef476a4a6a6341d3926520136614437f |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:07:06Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-ef476a4a6a6341d3926520136614437f2023-08-09T14:21:12ZengIOP PublishingNew Journal of Physics1367-26302022-01-0124505305610.1088/1367-2630/ac6f75Weighted network motifs as random walk patternsFrancesco Picciolo0Franco Ruzzenenti1Petter Holme2https://orcid.org/0000-0003-2156-1096Rossana Mastrandrea3https://orcid.org/0000-0001-8596-6389Department of Physical Sciences, Earth and Environment, University of Siena , 53100 Siena, ItalyIntegrated Research on Energy, Environment and Society, Faculty of Science and Engineering, University of Groningen , Nijenborgh 7, 9747AG Groningen, The NetherlandsDepartment of Computer Science, Aalto University , 02150 Espoo, Finland; Center for Computational Social Science, Kobe University , Kobe 657-8501, JapanIMT School for Advanced Studies , Lucca, Piazza S. Ponziano 6, 55100 Lucca, ItalyOver the last two decades, network theory has shown to be a fruitful paradigm in understanding the organization and functioning of real-world complex systems. One technique helpful to this endeavor is identifying functionally influential subgraphs, shedding light on underlying evolutionary processes. Such overrepresented subgraphs, motifs , have received much attention in simple networks, where edges are either on or off. However, for weighted networks, motif analysis is still undeveloped. Here, we proposed a novel methodology—based on a random walker taking a fixed maximum number of steps—to study weighted motifs of limited size. We introduce a sink node to balance the network and allow the detection of configurations within an a priori fixed number of steps for the random walker. We applied this approach to different real networks and selected a specific null model based on maximum-entropy to test the significance of weighted motifs occurrence. We found that identified similarities enable the classifications of systems according to functioning mechanisms associated with specific configurations: economic networks exhibit close patterns while differentiating from ecological systems without any a priori assumption.https://doi.org/10.1088/1367-2630/ac6f75weighted motifsrandom walknull modelscomplex networksweighted networks |
| spellingShingle | Francesco Picciolo Franco Ruzzenenti Petter Holme Rossana Mastrandrea Weighted network motifs as random walk patterns New Journal of Physics weighted motifs random walk null models complex networks weighted networks |
| title | Weighted network motifs as random walk patterns |
| title_full | Weighted network motifs as random walk patterns |
| title_fullStr | Weighted network motifs as random walk patterns |
| title_full_unstemmed | Weighted network motifs as random walk patterns |
| title_short | Weighted network motifs as random walk patterns |
| title_sort | weighted network motifs as random walk patterns |
| topic | weighted motifs random walk null models complex networks weighted networks |
| url | https://doi.org/10.1088/1367-2630/ac6f75 |
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