Modeling Spatial Social Complex Networks for Dynamical Processes
The study of social networks—where people are located, geographically, and how they might be connected to one another—is a current hot topic of interest, because of its immediate relevance to important applications, from devising efficient immunization techniques for the arrest of epidemics to the d...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1428719 |
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| _version_ | 1832556638052024320 |
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| author | Shandeepa Wickramasinghe Onyekachukwu Onyerikwu Jie Sun Daniel ben-Avraham |
| author_facet | Shandeepa Wickramasinghe Onyekachukwu Onyerikwu Jie Sun Daniel ben-Avraham |
| author_sort | Shandeepa Wickramasinghe |
| collection | DOAJ |
| description | The study of social networks—where people are located, geographically, and how they might be connected to one another—is a current hot topic of interest, because of its immediate relevance to important applications, from devising efficient immunization techniques for the arrest of epidemics to the design of better transportation and city planning paradigms to the understanding of how rumors and opinions spread and take shape over time. We develop a Spatial Social Complex Network (SSCN) model that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf’s law for the distribution of city populations as well as other observed hallmarks. We then simulate Milgram’s Small-World experiment on our SSCN model, obtaining good qualitative agreement with the known results and shedding light on the role played by various network attributes and the strategies used by the players in the game. This demonstrates the potential of the SSCN model for the simulation and study of the many social processes mentioned above, where both connectivity and geography play a role in the dynamics. |
| format | Article |
| id | doaj-art-d3ffb78e97b149e68d854df201f503ad |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d3ffb78e97b149e68d854df201f503ad2025-02-03T05:44:55ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/14287191428719Modeling Spatial Social Complex Networks for Dynamical ProcessesShandeepa Wickramasinghe0Onyekachukwu Onyerikwu1Jie Sun2Daniel ben-Avraham3Clarkson Center for Complex Systems Science (C3S2), Potsdam, NY 13699, USADepartment of Computer Science, Clarkson University, Potsdam, NY 13699, USAClarkson Center for Complex Systems Science (C3S2), Potsdam, NY 13699, USAClarkson Center for Complex Systems Science (C3S2), Potsdam, NY 13699, USAThe study of social networks—where people are located, geographically, and how they might be connected to one another—is a current hot topic of interest, because of its immediate relevance to important applications, from devising efficient immunization techniques for the arrest of epidemics to the design of better transportation and city planning paradigms to the understanding of how rumors and opinions spread and take shape over time. We develop a Spatial Social Complex Network (SSCN) model that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf’s law for the distribution of city populations as well as other observed hallmarks. We then simulate Milgram’s Small-World experiment on our SSCN model, obtaining good qualitative agreement with the known results and shedding light on the role played by various network attributes and the strategies used by the players in the game. This demonstrates the potential of the SSCN model for the simulation and study of the many social processes mentioned above, where both connectivity and geography play a role in the dynamics.http://dx.doi.org/10.1155/2018/1428719 |
| spellingShingle | Shandeepa Wickramasinghe Onyekachukwu Onyerikwu Jie Sun Daniel ben-Avraham Modeling Spatial Social Complex Networks for Dynamical Processes Complexity |
| title | Modeling Spatial Social Complex Networks for Dynamical Processes |
| title_full | Modeling Spatial Social Complex Networks for Dynamical Processes |
| title_fullStr | Modeling Spatial Social Complex Networks for Dynamical Processes |
| title_full_unstemmed | Modeling Spatial Social Complex Networks for Dynamical Processes |
| title_short | Modeling Spatial Social Complex Networks for Dynamical Processes |
| title_sort | modeling spatial social complex networks for dynamical processes |
| url | http://dx.doi.org/10.1155/2018/1428719 |
| work_keys_str_mv | AT shandeepawickramasinghe modelingspatialsocialcomplexnetworksfordynamicalprocesses AT onyekachukwuonyerikwu modelingspatialsocialcomplexnetworksfordynamicalprocesses AT jiesun modelingspatialsocialcomplexnetworksfordynamicalprocesses AT danielbenavraham modelingspatialsocialcomplexnetworksfordynamicalprocesses |