Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data
With few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data. On the other hand, automated collection of network-data often produces event data, L e., data encoding the exact time of interaction between social actors. In this paper we propose mod...
| Main Authors: | , , , |
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| Format: | Conference item |
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2009
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| _version_ | 1826258308903731200 |
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| author | Brandes, U Lerner, J Snijders, T IEEE |
| author_facet | Brandes, U Lerner, J Snijders, T IEEE |
| author_sort | Brandes, U |
| collection | OXFORD |
| description | With few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data. On the other hand, automated collection of network-data often produces event data, L e., data encoding the exact time of interaction between social actors. In this paper we propose models and methods to analyze such networks of dyadic events and to determine the factors that influence the frequency and quality of interaction. We apply our methods to empirical datasets about political conflicts and test several hypotheses concerning reciprocity and structural balance theory. © 2009 IEEE. |
| first_indexed | 2024-03-06T18:31:57Z |
| format | Conference item |
| id | oxford-uuid:09f18c7c-22c5-4c14-8027-e2b445cd660d |
| institution | University of Oxford |
| last_indexed | 2024-03-06T18:31:57Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:09f18c7c-22c5-4c14-8027-e2b445cd660d2022-03-26T09:21:05ZNetworks Evolving Step by Step: Statistical Analysis of Dyadic Event DataConference itemhttp://purl.org/coar/resource_type/c_5794uuid:09f18c7c-22c5-4c14-8027-e2b445cd660dSymplectic Elements at Oxford2009Brandes, ULerner, JSnijders, TIEEEWith few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data. On the other hand, automated collection of network-data often produces event data, L e., data encoding the exact time of interaction between social actors. In this paper we propose models and methods to analyze such networks of dyadic events and to determine the factors that influence the frequency and quality of interaction. We apply our methods to empirical datasets about political conflicts and test several hypotheses concerning reciprocity and structural balance theory. © 2009 IEEE. |
| spellingShingle | Brandes, U Lerner, J Snijders, T IEEE Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title | Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title_full | Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title_fullStr | Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title_full_unstemmed | Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title_short | Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data |
| title_sort | networks evolving step by step statistical analysis of dyadic event data |
| work_keys_str_mv | AT brandesu networksevolvingstepbystepstatisticalanalysisofdyadiceventdata AT lernerj networksevolvingstepbystepstatisticalanalysisofdyadiceventdata AT snijderst networksevolvingstepbystepstatisticalanalysisofdyadiceventdata AT ieee networksevolvingstepbystepstatisticalanalysisofdyadiceventdata |