The dynamics of U.S. college network formation on Facebook

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)


In the U.S., a significant portion of many people’s life-long social networks is formed in college. Yet our understanding of many aspects of this formation process, such as the role of time variation, heterogeneity between educational contexts, and the persistence of ties formed during college, is incomplete. In order to help fill some of these gaps, we use a population-level dataset of the social networks of 1,181 U.S. institutions of higher education, ranging from 2008 to 2019, to provide a detailed view of how the structure of college networks changes over time. The most prominent feature in the evolution of these networks is the burst in friending activity when students first enter college. Ties formed during this period play a strong role in shaping the structure of the networks overall and the students’ position within them. Subsequent starts and breaks from instruction further affect the volume of new tie formation. Homophily in tie formation likewise shows variation in time. Same-gender ties are more likely to form when students settle into housing, while sharing a major spurs friendships as students progress through their degree. Properties of the college, such as whether many students live on campus, also modulate these effects. Ties that form in different contexts and at different points in students’ college lives vary in their likelihood of remaining close years after graduation. Together, these findings suggest that educational context mediates network formation in multiple different ways.

Latest Publications

Sustainable AI: Environmental Implications, Challenges and Opportunities

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood

MLSys - 2022

Looper: an end-to-end ML platform for product decisions

Igor L. Markov, Hanson Wang, Nitya Kasturi, Shaun Singh, Mia Garrard, Yin Huang, Sze Wai Yuen, Sarah Tran, Zehui Wang, Igor Glotov, Tanvi Gupta, Peng Chen, Boshuang Huang, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou

KDD - 2022