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Linda Zhao, PhD

Assistant Professor of Sociology

University of Chicago

I am interested in how social contexts (such as levels of diversity or inequality in a population) can shape intergroup dynamics in social networks, how social networks and social contexts are linked to our behaviors and decisions, and how such networks can generate inequality. Throughout, my projects investigate intergroup dynamics, inequality, and social influence in networks within the areas of immigrant integration, policing, and public health. My current work leverages data from a range of contexts such as adolescent friendships in classrooms, officer networks in police departments, as well as quasi-experimental settings using computational models. 

Research Questions

1. How do network evolution and population structure jointly shape social outcomes?​

  • Key concepts: consolidation (the extent to which different social attributes are correlated in a population), network externalities (when adopting a behavior or practice is more likely given ties to prior adopters), and homophily (the tendency of "birds of a feather to flock together").

  • Findings: Under high consolidation, homophily exacerbates inequality in how behaviors diffuse through social networks. Yet under low consolidation, homophily has the opposite effect.

  • Data and methods: we use agent-based models to simulate populations, network formation, and then social diffusion. 


Zhao, L. and F. Garip. 2021. "Network Diffusion under Homophily and Consolidation as a Mechanism for Social Inequality." Sociological Methods and Research.

2. What can networks and population structure tell us about immigrant integration?

Zhao, L. “From Superdiversity to Consolidation: Implications of Structural Intersectionality for Interethnic Friendships.” Conditionally accepted at American Journal of Sociology




  • Key concepts: consolidation (the extent to which different social attributes are correlated in a population) and homophily (the tendency of "birds of a feather to flock together").

  • Findings: Consolidation between socioeconomic status and ethnicity in classrooms predicts greater ethnic homophily in classroom friendships. This suggests that multidimensional diversity could encourage interethnic friendships.

  • Data and methods: I use meta-analyses of exponential random graph models to analyze 11,011 students nested in 503 classroom friendship networks from the CILS4EU survey.


3. How do police officers’ networks relate to patterns of police misconduct?

Zhao, L. and A.V. Papachristos. 2020. “Network Position and Police Who Shoot.” The ANNALS of the American Academy of Political and Social Science. 










Zhao, L. and A.V. Papachristos. "Threats to Blue Networks: The Impact of Partner Injuries on Police Misconduct." R&R at American Sociological Review















  • Key concepts: shuffling (a phenomena we define as the re-assignment of officers across districts or other units).

  • Findings: Officers who bridge otherwise distinct groups of officers in networks of misconduct are more likely to shoot at civilians. This is strongly related to but not completely explained by organizational-level factors such as shuffling.

  • Data and methods: we re-create the network of police misconduct for the Chicago Police Department using more than 38,442 complaints filed against police officers between 2000 and 2003.

  • We suggest a networked group threat response that links peer injuries to police misconduct. We find that:

    • Network ties to injured officers predict higher levels of subsequent misconduct.

    • This effect is stronger for officers with stronger ties to the injured officer, but is present even for weaker ties.

    • The increase in misconduct following an injury was over two times larger when officers were injured by Black suspects, compared to when officers were injured by non-Black suspects.

  • Data and methods: we analyze allegations of misconduct, records on injuries, and several types of network ties using large-scale administrative data from the Chicago Police Department.


4. How can we use longitudinal data to understand the effects of neighborhoods on health?

Zhao, L., Hessel, P., Thomas, J.S. and J. Beckfield. Forthcoming. “Inequality in Place: Effects Exposure to Neighborhood Level Economic Inequality on Mortality.” Demography.

  • Key concepts: dynamic selection (in which A has an effect on B through C, but C can also shape future levels of A).

  • Findings: Recent exposure to higher income inequality in neighborhoods predicts higher mortality risk among individuals at ages 45 or above. We do not observe a cumulative effect.

  • Data and methods: we use the PSID and NCDB to follow 4,774 individuals, where they live, and how unequal their neighborhoods are, for over a decade. We use marginal structural models to account for dynamic selection.

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