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...
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Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
Read more about Edward L. Platt
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Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.
Read more about Edward L. Platt