20.10: What Have We Learned? — Read

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Networks come in various types and can be represented in probabilistic and algebraic views
Different centrality measures gauge the importance of nodes/edges from di↵erent aspects
PCA and SVD are useful for uncovering structural patterns in the network by performing matrix decomposition
Sparse PCA improves upon PCA by selecting a few most representative variables in the data and more accurately recovers community structure
Network communities have a variety of definitions, each of which has specific algorithms designed for community detection
Neural networks and deep learning networks are supervised learning machines that capture complex patterns in data.

20.10: What Have We Learned? is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

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