The last structural measure presented in this chapter is a little different from the ones seen so far. Betweenness, eigenvector, and closeness centrality all characterize a node by its relation to other nodes in the network. The measure presented in this section concerns the relationships between a node's neighbors, rather than those of the node itself. It is often useful to consider whether a node's neighbors tend to be connected to each other. In a social network, this question translates to asking whether the friend of a friend is also your friend, a property known as transitivity [to mathematicians who enjoy polysyllabic words]. The result of such relationships are triangles: three nodes, all mutually connected. The tendency for such triangles to arise is called clustering. When strong clustering is present, it often suggests robustness, and redundancy...
- Tech Categories
- Best Sellers
- New Releases
- Books
- Videos
- Audiobooks
Tech Categories Popular Audiobooks
- Articles
- Newsletters
- Free Learning
You're reading from Network Science with Python and NetworkX Quick Start Guide
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
Unlock this book and the full library FREE for 7 days
Author (1)
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