Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/4543563 |
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| _version_ | 1832561002581852160 |
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| author | Ashwin Bahulkar Boleslaw K. Szymanski Nitesh Chawla Omar Lizardo Kevin Chan |
| author_facet | Ashwin Bahulkar Boleslaw K. Szymanski Nitesh Chawla Omar Lizardo Kevin Chan |
| author_sort | Ashwin Bahulkar |
| collection | DOAJ |
| description | We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks. |
| format | Article |
| id | doaj-art-209b47570fa14acd9a0ceaacd53e133f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-209b47570fa14acd9a0ceaacd53e133f2025-02-03T01:26:10ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/45435634543563Influence of Personal Preferences on Link Dynamics in Social NetworksAshwin Bahulkar0Boleslaw K. Szymanski1Nitesh Chawla2Omar Lizardo3Kevin Chan4Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USARensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USAUniversity of Notre Dame, Notre Dame, IN 46556, USAUniversity of Notre Dame, Notre Dame, IN 46556, USAUS Army Research Laboratory, Adelphi, MD 20783, USAWe study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.http://dx.doi.org/10.1155/2017/4543563 |
| spellingShingle | Ashwin Bahulkar Boleslaw K. Szymanski Nitesh Chawla Omar Lizardo Kevin Chan Influence of Personal Preferences on Link Dynamics in Social Networks Complexity |
| title | Influence of Personal Preferences on Link Dynamics in Social Networks |
| title_full | Influence of Personal Preferences on Link Dynamics in Social Networks |
| title_fullStr | Influence of Personal Preferences on Link Dynamics in Social Networks |
| title_full_unstemmed | Influence of Personal Preferences on Link Dynamics in Social Networks |
| title_short | Influence of Personal Preferences on Link Dynamics in Social Networks |
| title_sort | influence of personal preferences on link dynamics in social networks |
| url | http://dx.doi.org/10.1155/2017/4543563 |
| work_keys_str_mv | AT ashwinbahulkar influenceofpersonalpreferencesonlinkdynamicsinsocialnetworks AT boleslawkszymanski influenceofpersonalpreferencesonlinkdynamicsinsocialnetworks AT niteshchawla influenceofpersonalpreferencesonlinkdynamicsinsocialnetworks AT omarlizardo influenceofpersonalpreferencesonlinkdynamicsinsocialnetworks AT kevinchan influenceofpersonalpreferencesonlinkdynamicsinsocialnetworks |