Neha Gondal: Social Networks, Inequalities, and Data Science
Neha Gondal is an assistant professor of Sociology at Boston University’s College of Arts and Sciences and founding member of the Faculty of Computing & Data Sciences (CDS). She has been involved in the hiring process for CDS Faculty members and also serves on the Academic Policy Committee. At the moment, her research work focuses on mapping University scholars who use computational and data science methods in the humanities, arts, and social sciences.
Gondal’s primary research explores the relationship between social networks, culture and the resulting social inequalities. These social networks include money lending in Renaissance Florence, health in 21st century public housing projects, and academic hiring.
To explore this vast research space, Gondal uses cutting-edge statistical techniques to model social networks. This includes different exponential random graph models and agent-based modeling – a technique that simulates the actions of individuals to study the effect each person has on the surrounding culture. Gondal’s research has found that networks help produce, legitimize, and preserve social inequalities in specific cultures.
What motivated you to pursue the study of social networks?
Data Science is a broad field. My interest is in social network analysis, the science of how persons and organizations are connected to one another and the implications of those connections. Currently, I am interested in how social networks can be used as a lens to study culture and inequalities.
How were you introduced to data science and how do you implement it into your work?
I became attracted to the study of social networks in graduate school. I took a few classes in the field at the time and then collaborated with my advisor on the disparate meanings of money lending in Renaissance Florence and its implications for the consolidation of status for elite Florentines of the time.
I draw on diverse statistical and simulation techniques to model social networks including varieties of exponential random graph models (ERGM) and agent-based modeling (ABM). I have used these approaches to study a variety of contexts including elite consolidation through money-lending ties in Renaissance Florence, inequalities in academic communities evident in citation and hiring networks, and the clustering of unhealthy outcomes in Boston’s public housing developments.
What have been some challenges you faced between sociology and data science?
Network analysis largely arose within the fields of sociology and anthropology. Today, it is a widely used framework in a variety of disciplines. This expansion of the framework is phenomenal! It has created opportunities for cross-disciplinary collaborations and made network analysis mainstream. At the same time, it has created some challenges because of differentiation in the way networks are studied and understood across disciplines.
What cultural impacts do you expect to see in the next two decades?
Humans are becoming increasingly connected at an increasingly fast pace. One implication of this is that things spread across groups at a rapid pace. Covid-19 is an example of this phenomenon. Cultural materials – ideas, beliefs, actions – also disseminate in similar ways. But human connectivity tends to be homogenous – people are more likely to be connected to others who are similar to themselves. This means that the dissemination of cultural materials can be similarly clumpy. Some ideas, such as proclivity towards vaccines, can disseminate widely among some communities but not at all among others. This ‘differential diffusion’ exacerbates differences between groups that may also be on equal footings for other reasons such as race, class, or gender. Thus, in addition to access and other drivers of inequalities, culture and connectivity can contribute to worsening social hierarchies.
What are you looking forward to in your role at CDS?
I am thrilled to have a role in shaping the structure and content of academic degrees offered at CDS. I am keen that academic programs at CDS draw on the existing strengths of faculty and research programs across the disciplines and schools at BU.