Summary

This chapter has shown how to analyze the microscale structure of networks by calculating centrality measures and other node-based measures of network structure. Betweenness centrality identifies bridges and brokers: edges and nodes that connect otherwise poorly connected parts of a network. Eigenvector centrality identifies nodes that are connected to other well-connected nodes. Closeness centrality identifies nodes that are, on average, closest to other nodes. Finally, the triangle count and local clustering coefficient quantify how well-connected a node's friends are. By examining a historical social network of suffragette activists, we saw that ranking highly on one centrality value doesn't necessarily mean a node ranks highly on others. While sometimes correlated, different centrality values measure different things, so meaningful results require choosing...