Robustness of Interdependent Random Geometric Networks
We propose an interdependent random ggraph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2020
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| Online Access: | https://hdl.handle.net/1721.1/126323 |
| _version_ | 1826193548530155520 |
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| author | Zhang, Jianan Modiano, Eytan H |
| author2 | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems |
| author_facet | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Zhang, Jianan Modiano, Eytan H |
| author_sort | Zhang, Jianan |
| collection | MIT |
| description | We propose an interdependent random ggraph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99 percent confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks. |
| first_indexed | 2024-09-23T09:40:52Z |
| format | Article |
| id | mit-1721.1/126323 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:40:52Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1263232022-09-26T13:04:31Z Robustness of Interdependent Random Geometric Networks Zhang, Jianan Modiano, Eytan H Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Department of Aeronautics and Astronautics We propose an interdependent random ggraph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99 percent confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks. United States. Defense Threat Reduction Agency (Grant HDTRA1-14-1-0058) United States. Defense Threat Reduction Agency (Grant HDTRA1-13-1-0021) National Science Foundation (U.S.) (Grant CMMI-1638234) 2020-07-22T19:14:04Z 2020-07-22T19:14:04Z 2019-07 2019-10-30T15:48:32Z Article http://purl.org/eprint/type/JournalArticle 2327-4697 https://hdl.handle.net/1721.1/126323 Zhang, Jianan, Edmund Yeh and Eytan Modiano. “Robustness of Interdependent Random Geometric Networks.” IEEE transactions on network science and engineering, vol. 6, no. 3, 2019, pp. 474-487 © 2019 The Author(s) en 10.1109/TNSE.2018.2846720 IEEE transactions on network science and engineering Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Zhang, Jianan Modiano, Eytan H Robustness of Interdependent Random Geometric Networks |
| title | Robustness of Interdependent Random Geometric Networks |
| title_full | Robustness of Interdependent Random Geometric Networks |
| title_fullStr | Robustness of Interdependent Random Geometric Networks |
| title_full_unstemmed | Robustness of Interdependent Random Geometric Networks |
| title_short | Robustness of Interdependent Random Geometric Networks |
| title_sort | robustness of interdependent random geometric networks |
| url | https://hdl.handle.net/1721.1/126323 |
| work_keys_str_mv | AT zhangjianan robustnessofinterdependentrandomgeometricnetworks AT modianoeytanh robustnessofinterdependentrandomgeometricnetworks |