The paper analyzes the structure of Facebook's social graph, the largest social network ever studied. It examines key features such as the number of users, friendships, degree distribution, path lengths, clustering, and mixing patterns. The results show that the network is nearly fully connected, with 99.91% of users in a single large connected component, confirming the "six degrees of separation" phenomenon. The graph is sparse overall, but local neighborhoods are densely connected. The network exhibits degree assortativity, where friends have more friends than you, and shows strong age and nationality-based mixing patterns, but not significant gender homophily. The average distance between users is about 4.7, with most pairs connected within five degrees. The network is highly clustered, with dense cores in neighborhoods. The study also finds that users with more friends have more friends-of-friends, and that friendships are influenced by age, gender, and country of origin. The network is modular, with communities aligned with national borders. The analysis highlights the importance of understanding the structure of large social networks for algorithm development and social science research.The paper analyzes the structure of Facebook's social graph, the largest social network ever studied. It examines key features such as the number of users, friendships, degree distribution, path lengths, clustering, and mixing patterns. The results show that the network is nearly fully connected, with 99.91% of users in a single large connected component, confirming the "six degrees of separation" phenomenon. The graph is sparse overall, but local neighborhoods are densely connected. The network exhibits degree assortativity, where friends have more friends than you, and shows strong age and nationality-based mixing patterns, but not significant gender homophily. The average distance between users is about 4.7, with most pairs connected within five degrees. The network is highly clustered, with dense cores in neighborhoods. The study also finds that users with more friends have more friends-of-friends, and that friendships are influenced by age, gender, and country of origin. The network is modular, with communities aligned with national borders. The analysis highlights the importance of understanding the structure of large social networks for algorithm development and social science research.