Computational Social Science Forum – Social network analysis and the missing data challenge

CSS Training Program

March 15, 2021
12:00pm to 1:30pm
Virtual Participation


Computational Social Science Forum
Date: Monday, March 15, 2021
Time: 12:00-1:30 PM Pacific Time
Location: Register to receive the schedule and access links.

Social network analysis and the missing data challenge

Martin Eiermann, PhD Student, Sociology, UC Berkeley 
Abstract: It is difficult to assemble truly comprehensive datasets about the social world. Most commonly, scholars address this challenge by focusing on representativeness rather than completeness: Through careful sampling, it is possible to construct partial datasets that remain generalizable to an underlying population. Yet this approach is less likely to succeed in social network analysis because sampled networks can differ significantly and unexpectedly from their "true" manifestations, especially when networks are sparse. This complicates inferences on the basis of sampled network data and imposes greater burdens on researchers to ensure the completeness of their datasets. I illustrate the scope of this challenge using observational data about social movement participation, and I highlight two potential remedies: a triangulation strategy that combines different research methods to increase the comprehensiveness of recorded network data; and a validation strategy that relies on simulated missing data scenarios to quantify the uncertainty associated with random and non-random measurement errors. 

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Weekly meetings are hosted by researchers from BIDS and D-Lab, and participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. We welcome social scientists researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. This Forum is organized as part of the Computational Social Science Training Program, and interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact for more information.


Martin Eiermann

Sociology, UC Berkeley