NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit
Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. N...
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| Format: | Article |
| Language: | English |
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GigaScience Press
2022-01-01
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| Series: | GigaByte |
| Online Access: | https://gigabytejournal.com/articles/37 |
| _version_ | 1797884348271165440 |
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| author | Niema Moshiri |
| author_facet | Niema Moshiri |
| author_sort | Niema Moshiri |
| collection | DOAJ |
| description |
Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies.
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| first_indexed | 2024-04-10T04:05:09Z |
| format | Article |
| id | doaj.art-864f8614f9874cc182c6c46030aed382 |
| institution | Directory Open Access Journal |
| issn | 2709-4715 |
| language | English |
| last_indexed | 2024-04-10T04:05:09Z |
| publishDate | 2022-01-01 |
| publisher | GigaScience Press |
| record_format | Article |
| series | GigaByte |
| spelling | doaj.art-864f8614f9874cc182c6c46030aed3822023-03-13T06:27:14ZengGigaScience PressGigaByte2709-47152022-01-0110.46471/gigabyte.37NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkitNiema Moshiri 0https://orcid.org/0000-0003-2209-8128Department of Computer Science & Engineering, UC San Diego, La Jolla, 92093, USA Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies. https://gigabytejournal.com/articles/37 |
| spellingShingle | Niema Moshiri NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit GigaByte |
| title | NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit |
| title_full | NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit |
| title_fullStr | NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit |
| title_full_unstemmed | NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit |
| title_short | NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit |
| title_sort | niemagraphgen a memory efficient global scale contact network simulation toolkit |
| url | https://gigabytejournal.com/articles/37 |
| work_keys_str_mv | AT niemamoshiri niemagraphgenamemoryefficientglobalscalecontactnetworksimulationtoolkit |