Analysis of Link Formation, Persistence and Dissolution in NetSense Data

TL;DR

This paper analyzes how student traits and activities influence the formation, persistence, and dissolution of social links in a university setting, using survey and electronic communication data over three years.

Contribution

It introduces a comprehensive analysis combining survey data and electronic logs to predict social network evolution based on student traits and activities.

Findings

Traits and activities correlate with link formation and persistence.

Disagreements strongly relate to link dissolution.

Machine learning can predict network changes.

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 are 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.