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 world-class faculty at Carnegie Mellon lead the field in research exploring these complex social networks. Ranging from the analysis and identification of terrorist threats to sophisticated studies of online community health and efficacy, we seek to deepen our understanding of the science underpinning many of the interconnected networks which comprise much of our lived experience.
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.
Example Research
An Experiment in Hiring Discrimination via Online Social Networks
This study investigates whether employers use social media to search for job candidates and if this influences their hiring decisions based on protected characteristics. Through a field experiment with over 4,000 job applications, the authors created controlled social media profiles for candidates differing only in religious affiliation and sexual orientation. While the experiment found no difference in callback rates for gay vs. straight candidates, Muslim candidates received 13% fewer callbacks than Christian candidates, with significant regional biases. The findings reveal nuanced discrimination patterns and underscore the need for awareness around the implications of online disclosures in hiring practices.
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.