This paper provides a comprehensive analysis of the structure of the Facebook social graph, the largest social network ever studied. The authors compute various features of the graph, including the number of users and friendships, degree distribution, path lengths, clustering, and mixing patterns. Key findings include: 1. **Global Structure**: The social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component. The 'six degrees of separation' phenomenon is confirmed on a global scale, with an average distance of 4.7 between any two users. 2. **Local Structure**: Despite the overall sparsity of the network, user neighborhoods exhibit surprisingly dense structures. The average local clustering coefficient is high, and the degeneracy of neighborhood graphs is significant, indicating tight-knit communities within these neighborhoods. 3. **Degree Correlations**: There is positive assortativity in the degree distribution, meaning that users with more friends tend to have friends with more friends. This phenomenon is observed on a grand scale, with 83.6% of users having fewer friends than the median friend count of their friends. 4. **User Traits and Mixing Patterns**: The network shows interesting mixing patterns based on user traits such as age, gender, and country of origin. Users tend to have friends who are similar in age and from the same country. Gender homophily is minimal, while country-based community structures are evident, driven by geographical and historical factors. 5. **Algorithmic Implications**: The findings have significant implications for graph traversal algorithms, as the positive degree correlation and high clustering can lead to a large number of potential queries and redundant searches. The study aims to advance the understanding of social networks and inform the development of graph algorithms and network analysis tools.This paper provides a comprehensive analysis of the structure of the Facebook social graph, the largest social network ever studied. The authors compute various features of the graph, including the number of users and friendships, degree distribution, path lengths, clustering, and mixing patterns. Key findings include: 1. **Global Structure**: The social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component. The 'six degrees of separation' phenomenon is confirmed on a global scale, with an average distance of 4.7 between any two users. 2. **Local Structure**: Despite the overall sparsity of the network, user neighborhoods exhibit surprisingly dense structures. The average local clustering coefficient is high, and the degeneracy of neighborhood graphs is significant, indicating tight-knit communities within these neighborhoods. 3. **Degree Correlations**: There is positive assortativity in the degree distribution, meaning that users with more friends tend to have friends with more friends. This phenomenon is observed on a grand scale, with 83.6% of users having fewer friends than the median friend count of their friends. 4. **User Traits and Mixing Patterns**: The network shows interesting mixing patterns based on user traits such as age, gender, and country of origin. Users tend to have friends who are similar in age and from the same country. Gender homophily is minimal, while country-based community structures are evident, driven by geographical and historical factors. 5. **Algorithmic Implications**: The findings have significant implications for graph traversal algorithms, as the positive degree correlation and high clustering can lead to a large number of potential queries and redundant searches. The study aims to advance the understanding of social networks and inform the development of graph algorithms and network analysis tools.