Abstract
We study a unique behavioral network data set (based on periodic surveys and on electronic logs of dyadic contact via smartphones) collected at the University of Notre Dame. The participants are a sample of members of the entering class of freshmen in the fall of 2011 whose opinions on a wide variety of political and social issues and activities on campus were regularly recorded - at the beginning and end of each semester - for the first three years of their residence on campus. We create a communication activity network implied by call and text data, and a friendship network based on surveys. Both networks are limited to students participating in the NetSense surveys. We aim at finding student traits and activities on which agreements correlate well with formation and persistence of links while disagreements is highly correlated with non-existence or dissolution of links in the two social networks that we created. Using statistical analysis and machine learning, we observe several traits and activities displaying such correlations, thus being of potential use to predict social network evolution.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
| Editors | Ravi Kumar, James Caverlee, Hanghang Tong |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1197-1204 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509028467 |
| DOIs | |
| State | Published - Nov 21 2016 |
| Event | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: Aug 18 2016 → Aug 21 2016 |
Publication series
| Name | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
|---|
Other
| Other | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 8/18/16 → 8/21/16 |
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Keywords
- NetSense
- evolving networks
- link persistence
- link prediction
- social networks
ASJC Scopus subject areas
- Computer Networks and Communications
- Sociology and Political Science
- Communication
Cite this
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Analysis of link formation, persistence and dissolution in NetSense data. / Bahulkar, Ashwin; Szymanski, Boleslaw K.; Lizardo, Omar; Dong, Yuxiao; Yang, Yang; Chawla, Nitesh V.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1197-1204 7752391 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Analysis of link formation, persistence and dissolution in NetSense data
AU - Bahulkar, Ashwin
AU - Szymanski, Boleslaw K.
AU - Lizardo, Omar
AU - Dong, Yuxiao
AU - Yang, Yang
AU - Chawla, Nitesh V.
PY - 2016/11/21
Y1 - 2016/11/21
N2 - We study a unique behavioral network data set (based on periodic surveys and on electronic logs of dyadic contact via smartphones) collected at the University of Notre Dame. The participants are a sample of members of the entering class of freshmen in the fall of 2011 whose opinions on a wide variety of political and social issues and activities on campus were regularly recorded - at the beginning and end of each semester - for the first three years of their residence on campus. We create a communication activity network implied by call and text data, and a friendship network based on surveys. Both networks are limited to students participating in the NetSense surveys. We aim at finding student traits and activities on which agreements correlate well with formation and persistence of links while disagreements is highly correlated with non-existence or dissolution of links in the two social networks that we created. Using statistical analysis and machine learning, we observe several traits and activities displaying such correlations, thus being of potential use to predict social network evolution.
AB - We study a unique behavioral network data set (based on periodic surveys and on electronic logs of dyadic contact via smartphones) collected at the University of Notre Dame. The participants are a sample of members of the entering class of freshmen in the fall of 2011 whose opinions on a wide variety of political and social issues and activities on campus were regularly recorded - at the beginning and end of each semester - for the first three years of their residence on campus. We create a communication activity network implied by call and text data, and a friendship network based on surveys. Both networks are limited to students participating in the NetSense surveys. We aim at finding student traits and activities on which agreements correlate well with formation and persistence of links while disagreements is highly correlated with non-existence or dissolution of links in the two social networks that we created. Using statistical analysis and machine learning, we observe several traits and activities displaying such correlations, thus being of potential use to predict social network evolution.
KW - NetSense
KW - evolving networks
KW - link persistence
KW - link prediction
KW - social networks
UR - http://www.scopus.com/inward/record.url?scp=85006699006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006699006&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2016.7752391
DO - 10.1109/ASONAM.2016.7752391
M3 - Conference contribution
AN - SCOPUS:85006699006
T3 - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
SP - 1197
EP - 1204
BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
A2 - Kumar, Ravi
A2 - Caverlee, James
A2 - Tong, Hanghang
PB - Institute of Electrical and Electronics Engineers Inc.
ER -