April 2019
Beginner
190 pages
4h 54m
English
Content preview from Network Science with Python and NetworkX Quick Start Guide
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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 ...
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