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4. Cliques, Clusters and Components > Hierarchical Clustering

Hierarchical Clustering

The next class of algorithms we will touch on—though briefly—is clustering algorithms. The universe of clustering algorithms is large and varied, and perhaps best addressed by other books—but I will briefly touch on the application of clustering algorithms to social network analysis and provide a quick example of useful insights that can be derived from them.

Let’s first start by returning to the notion of distance. We can define distance in many ways—from geographical distance to ground travel distance to time-based distance (i.e., how long it takes to get from point A to point B), and so on. In social networks, we find two types of distance most useful; a graph distance (or path length) between pairs of nodes, and a similarity-based distance (that is, we consider nodes to be closer together if they are similar in some way).


  

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