Network Science and Social Networks
Social networks constitute a significant portion of our daily lives. It is crucial that we be able to better understand what drives them.
The whole world is networked. From the most inconspicuous administrative to the most notorious arms dealer, we are all connected through those with whom we interact. And, as technology becomes a more pervasive force in our daily lives, many of these networks can be observed, measured, analyzed, and understood through the data that they produce.
Take, for example, a major corporation’s efforts to reduce the impact turnover in key positions may cause. Oftentimes, knowledge and job skill is only part of the equation. A particularly effective employee may be so because of the people she knows and the groups she moves between. Network Science and Social Networks endeavors to understand this network’s structure, the employee's position within it, and the linkage between actors in order to provide insight into the mechanisms which underlie its operation. With this understanding, we can more effectively analyze the network as well as reason about and predict the behaviors of those within it.
Our research in Network Science and Social Networks takes an interdisciplinary approach, blending computational methods with insights from social sciences to address real-world challenges like organizational dynamics, online polarization, and social equity.
Our world-class faculty at Carnegie Mellon lead the field in research exploring these complex social networks. The faculty members involved are:
- Yuvraj Agarwal
- Andrew Begel
- Travis Breaux
- Justin Chan
- Nicolas Christin
- Lorrie Cranor
- Fei Fang
- Matt Fredrikson
- Mayank Goel
- Hoda Heidari
- Patrick Park
- Sarah Scheffler
- Bogdan Vasilescu
- Steven Wu
Each brings unique expertise to the field, from computational methods to social dynamics.
Do you ever wonder who’s the power behind the throne? Or how they find individuals who may pose an insider threat to sensitive information? Questions like these can be addressed using network analytics.
Research Opportunities
As a PhD student in Network Science and Social Networks, you'll have the chance to work on cutting-edge projects that explore the intersection of technology and society. Our students collaborate with faculty on a variety of topics, including:
- Analyzing social media data to understand online behavior and polarization.
- Conducting field experiments to study bias in hiring processes.
- Building simulations to model network behaviors in economic and social contexts.
Through these projects, students gain hands-on experience in interdisciplinary research, working with large-scale datasets, and applying computational methods to real-world problems.Funding and Support
All admitted PhD students in the Societal Computing program receive full funding, which includes tuition and a living stipend. This support is provided through:
- Research assistantships on faculty-led projects.
- Teaching assistantships within the School of Computer Science.
- Fellowships from CMU or external sources, such as the National Science Foundation.
Additionally, students have access to advanced computing facilities, large-scale datasets, and collaborations with partners like the Center for Computational Analysis of Social and Organizational Systems (CASOS), enhancing their research capabilities.Graduates specializing in Network Science and Social Networks are equipped to address complex system challenges through computational and mathematical analysis of relationships and structures. They pursue diverse, impactful careers across multiple sectors:
- In Academia:
- Faculty advancing network science methodologies in computer science, computational social science, and interdisciplinary departments.
- Research scientists developing innovative algorithms for dynamic, multi-modal networks.
- Academic leaders connecting computational methods to applications in healthcare, economics, and political science.
- In Industry:
- Data scientists at tech firms designing recommendation systems rooted in social network principles.
- Research engineers building computational models to study information diffusion and counter misinformation.
- Product managers enhancing user experiences on social platforms through network analysis.
- In Government and Policy:
- Analysts using network methods to optimize organizational dynamics and information flow.
- Researchers modeling crisis response networks and critical infrastructure resilience.
- Policy advisors crafting evidence-based interventions based on network vulnerability assessments.
Our distinctive approach blends rigorous graph-theoretic foundations with computational modeling and domain-specific applications. Students gain expertise in both the mathematical roots of network structures and the practical skills for large-scale network analysis, preparing them to lead pioneering research and development at the intersection of technology and social systems.
Example Research
An Experiment in Hiring Discrimination via Online Social Networks
In a field experiment involving 4,000 job applications, researchers found that Muslim candidates received 13% fewer callbacks than Christian candidates, with the effect being more pronounced in Republican-leaning states. This study, led by Professor Alessandro Acquisti, used a combination of social media analysis and experimental design to uncover biases in hiring practices. The findings highlight the need for transparency and fairness in recruitment processes, especially when social media information is considered.
The Life of a Tie: Social Origins of Network Diversity
In this study, the authors explore the resilience and evolution of long-term social ties on Twitter, examining how these connections withstand ideological and cognitive divides. Analyzing over 443,000 bi-directional mention ties from before 2015 and during the COVID-19 pandemic, they find that strong pre-existing ties can endure even when discussing contentious topics, challenging traditional models that suggest social ties grow stronger with cognitive similarity. These findings highlight the potential of enduring connections to bridge polarized communities, suggesting that long-standing relationships may play a key role in reducing societal divides on divisive issues.
Network Wormholes
Contrary to the common assumption that long-range connections in social networks are weak and emotionally distant, researchers have found evidence suggesting otherwise. Analyzing data from 11 culturally diverse, population-scale networks across four continents—including 56 million Twitter users and 58 million mobile phone subscribers—the study reveals that long-range ties are almost as strong as those within close-knit groups of friends. These robust, high-bandwidth connections hold significant implications for social diffusion and integration, suggesting a reevaluation of how distant ties contribute to the cohesion of large social networks.
Networks, Property, and the Division of Labor
This research explores how different network structures influence the evolution of a division of labor among economic producers, focusing on conditions that promote or inhibit specialization. Using a simulation-based approach, the authors model decentralized coordination among agents with interdependent roles, revealing that certain network topologies—especially those with higher structural constraints—facilitate a more stable division of labor. The study also finds that agents’ ability to store surplus goods plays a crucial role in achieving sustainable specialization, suggesting practical insights into how network dynamics and property rights impact economic coordination.