The Structure of U.S. College Networks on Facebook
Jan Overgoor, Bogdan State, Lada Adamic

TL;DR
This paper presents a large-scale dataset of U.S. college social networks from Facebook, introduces a new methodology for comparing networks of different sizes, and reveals patterns related to school type and cohort similarity.
Contribution
It provides the first comprehensive dataset linking Facebook social networks with college data and develops a novel method for comparing networks across diverse institutions.
Findings
Networks of the same school across cohorts are more similar than across different schools.
Public/private and graduation rate are key factors in network structure differences.
Private school students tend to have larger, more clustered, and more homophilous networks.
Abstract
Anecdotally, social connections made in university have life-long impact. Yet knowledge of social networks formed in college remains episodic, due in large part to the difficulty and expense involved in collecting a suitable dataset for comprehensive analysis. To advance and systematize insight into college social networks, we describe a dataset of the largest online social network platform used by college students in the United States. We combine de-identified and aggregated Facebook data with College Scorecard data, campus-level information provided by U.S. Department of Education, to produce a dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and universities, spanning 7.6 million students. To perform the difficult task of comparing these networks of different sizes we develop a new methodology. We compute features over sampled ego-graphs, train binary…
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