This study investigates how mobile phone data can be used to infer friendship network structures. Researchers analyzed data from 94 subjects over nine months, collecting information on call logs, Bluetooth proximity, cell tower IDs, and phone usage. They compared this "behavioral" data with self-reported relationships and found that 95% of friendships could be accurately inferred from mobile phone data alone. The study shows that behavioral patterns, such as proximity and communication, can predict individual-level outcomes like job satisfaction. The research highlights the limitations of self-report data in social network analysis, which often suffers from recall biases and limited accuracy. By contrast, mobile phone data provides objective measures of behavior that can reveal distinct patterns in physical proximity and communication that are strongly associated with friendship. For example, friends tend to have more frequent and longer proximity interactions outside of work hours, and their communication patterns differ significantly from those of nonfriends. The study also demonstrates that behavioral data can be used to predict individual satisfaction with work groups. Friends who are physically close at work are more likely to be satisfied with their work group, while calling friends at work is associated with lower satisfaction. These findings suggest that behavioral data can provide a more accurate and comprehensive understanding of social relationships than self-reported data. The study's results have important implications for social network analysis, as they show that mobile phone data can be used to infer social relationships and predict individual outcomes. This approach could be used to study social dynamics in organizations, communities, and potentially even societies. However, the study also raises important privacy concerns about the collection and use of mobile phone data.This study investigates how mobile phone data can be used to infer friendship network structures. Researchers analyzed data from 94 subjects over nine months, collecting information on call logs, Bluetooth proximity, cell tower IDs, and phone usage. They compared this "behavioral" data with self-reported relationships and found that 95% of friendships could be accurately inferred from mobile phone data alone. The study shows that behavioral patterns, such as proximity and communication, can predict individual-level outcomes like job satisfaction. The research highlights the limitations of self-report data in social network analysis, which often suffers from recall biases and limited accuracy. By contrast, mobile phone data provides objective measures of behavior that can reveal distinct patterns in physical proximity and communication that are strongly associated with friendship. For example, friends tend to have more frequent and longer proximity interactions outside of work hours, and their communication patterns differ significantly from those of nonfriends. The study also demonstrates that behavioral data can be used to predict individual satisfaction with work groups. Friends who are physically close at work are more likely to be satisfied with their work group, while calling friends at work is associated with lower satisfaction. These findings suggest that behavioral data can provide a more accurate and comprehensive understanding of social relationships than self-reported data. The study's results have important implications for social network analysis, as they show that mobile phone data can be used to infer social relationships and predict individual outcomes. This approach could be used to study social dynamics in organizations, communities, and potentially even societies. However, the study also raises important privacy concerns about the collection and use of mobile phone data.