Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks
Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, socia...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9667531/ |
| _version_ | 1818744557254213632 |
|---|---|
| author | Djibril Mboup Cherif Diallo Hocine Cherifi |
| author_facet | Djibril Mboup Cherif Diallo Hocine Cherifi |
| author_sort | Djibril Mboup |
| collection | DOAJ |
| description | Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks. |
| first_indexed | 2024-12-18T02:46:12Z |
| format | Article |
| id | doaj.art-5830d8f136334583a8d9c11c8177ad54 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T02:46:12Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5830d8f136334583a8d9c11c8177ad542022-12-21T21:23:34ZengIEEEIEEE Access2169-35362022-01-01105912593510.1109/ACCESS.2021.31401369667531Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World NetworksDjibril Mboup0https://orcid.org/0000-0003-4986-9922Cherif Diallo1https://orcid.org/0000-0001-6606-7337Hocine Cherifi2https://orcid.org/0000-0001-9124-4921LACCA Laboratory, Gaston Berger University, Saint-Louis, PB, SenegalLACCA Laboratory, Gaston Berger University, Saint-Louis, PB, SenegalLIB EA, 7534, University of Burgundy Franche-Comté, Dijon, FranceMobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks.https://ieeexplore.ieee.org/document/9667531/Temporal networkscontact networksproximity networkstime varying graphshuman mobility networkshuman dynamics |
| spellingShingle | Djibril Mboup Cherif Diallo Hocine Cherifi Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks IEEE Access Temporal networks contact networks proximity networks time varying graphs human mobility networks human dynamics |
| title | Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks |
| title_full | Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks |
| title_fullStr | Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks |
| title_full_unstemmed | Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks |
| title_short | Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks |
| title_sort | temporal networks based on human mobility models a comparative analysis with real world networks |
| topic | Temporal networks contact networks proximity networks time varying graphs human mobility networks human dynamics |
| url | https://ieeexplore.ieee.org/document/9667531/ |
| work_keys_str_mv | AT djibrilmboup temporalnetworksbasedonhumanmobilitymodelsacomparativeanalysiswithrealworldnetworks AT cherifdiallo temporalnetworksbasedonhumanmobilitymodelsacomparativeanalysiswithrealworldnetworks AT hocinecherifi temporalnetworksbasedonhumanmobilitymodelsacomparativeanalysiswithrealworldnetworks |