Classes of random walks on temporal networks with competing timescales
Abstract Random walks find applications in many areas of science and are the heart of essential network analytic tools. When defined on temporal networks, even basic random walk models may exhibit a rich spectrum of behaviours, due to the co-existence of different timescales in the system. Here, we...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-09-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41109-019-0204-6 |
| _version_ | 1819005190909460480 |
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| author | Julien Petit Renaud Lambiotte Timoteo Carletti |
| author_facet | Julien Petit Renaud Lambiotte Timoteo Carletti |
| author_sort | Julien Petit |
| collection | DOAJ |
| description | Abstract Random walks find applications in many areas of science and are the heart of essential network analytic tools. When defined on temporal networks, even basic random walk models may exhibit a rich spectrum of behaviours, due to the co-existence of different timescales in the system. Here, we introduce random walks on general stochastic temporal networks allowing for lasting interactions, with up to three competing timescales. We then compare the mean resting time and stationary state of different models. We also discuss the accuracy of the mathematical analysis depending on the random walk model and the structure of the underlying network, and pay particular attention to the emergence of non-Markovian behaviour, even when all dynamical entities are governed by memoryless distributions. |
| first_indexed | 2024-12-20T23:48:51Z |
| format | Article |
| id | doaj.art-b3cc48e63b1e4613b1d56d528ab7a541 |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-20T23:48:51Z |
| publishDate | 2019-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-b3cc48e63b1e4613b1d56d528ab7a5412022-12-21T19:22:53ZengSpringerOpenApplied Network Science2364-82282019-09-014112010.1007/s41109-019-0204-6Classes of random walks on temporal networks with competing timescalesJulien Petit0Renaud Lambiotte1Timoteo Carletti2Department of Mathematics, Royal Military AcademyMathematical InstituteDepartment of Mathematics and naXys, Namur Institute for Complex SystemsAbstract Random walks find applications in many areas of science and are the heart of essential network analytic tools. When defined on temporal networks, even basic random walk models may exhibit a rich spectrum of behaviours, due to the co-existence of different timescales in the system. Here, we introduce random walks on general stochastic temporal networks allowing for lasting interactions, with up to three competing timescales. We then compare the mean resting time and stationary state of different models. We also discuss the accuracy of the mathematical analysis depending on the random walk model and the structure of the underlying network, and pay particular attention to the emergence of non-Markovian behaviour, even when all dynamical entities are governed by memoryless distributions.http://link.springer.com/article/10.1007/s41109-019-0204-6Random walkTemporal networkMemory |
| spellingShingle | Julien Petit Renaud Lambiotte Timoteo Carletti Classes of random walks on temporal networks with competing timescales Applied Network Science Random walk Temporal network Memory |
| title | Classes of random walks on temporal networks with competing timescales |
| title_full | Classes of random walks on temporal networks with competing timescales |
| title_fullStr | Classes of random walks on temporal networks with competing timescales |
| title_full_unstemmed | Classes of random walks on temporal networks with competing timescales |
| title_short | Classes of random walks on temporal networks with competing timescales |
| title_sort | classes of random walks on temporal networks with competing timescales |
| topic | Random walk Temporal network Memory |
| url | http://link.springer.com/article/10.1007/s41109-019-0204-6 |
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