Generating realistic scaled complex networks
Abstract Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-10-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41109-017-0054-z |
| _version_ | 1818285132277088256 |
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| author | Christian L. Staudt Michael Hamann Alexander Gutfraind Ilya Safro Henning Meyerhenke |
| author_facet | Christian L. Staudt Michael Hamann Alexander Gutfraind Ilya Safro Henning Meyerhenke |
| author_sort | Christian L. Staudt |
| collection | DOAJ |
| description | Abstract Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size. |
| first_indexed | 2024-12-13T01:03:50Z |
| format | Article |
| id | doaj.art-ab92ce1bd4854cb4aa92e49167ac565c |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-13T01:03:50Z |
| publishDate | 2017-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-ab92ce1bd4854cb4aa92e49167ac565c2022-12-22T00:04:36ZengSpringerOpenApplied Network Science2364-82282017-10-012112910.1007/s41109-017-0054-zGenerating realistic scaled complex networksChristian L. Staudt0Michael Hamann1Alexander Gutfraind2Ilya Safro3Henning Meyerhenke4Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT)Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT)Laboratory for Mathematical Analysis of Complexity and Conflicts, University of Illinois at ChicagoSchool of Computing, Clemson UniversityInstitute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT)Abstract Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.http://link.springer.com/article/10.1007/s41109-017-0054-zNetwork generationMultiscale modelingNetwork modelingCommunities |
| spellingShingle | Christian L. Staudt Michael Hamann Alexander Gutfraind Ilya Safro Henning Meyerhenke Generating realistic scaled complex networks Applied Network Science Network generation Multiscale modeling Network modeling Communities |
| title | Generating realistic scaled complex networks |
| title_full | Generating realistic scaled complex networks |
| title_fullStr | Generating realistic scaled complex networks |
| title_full_unstemmed | Generating realistic scaled complex networks |
| title_short | Generating realistic scaled complex networks |
| title_sort | generating realistic scaled complex networks |
| topic | Network generation Multiscale modeling Network modeling Communities |
| url | http://link.springer.com/article/10.1007/s41109-017-0054-z |
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