The two related concepts of clustering and homophily are essential components of network analysis in which they can provide key insights into network behavior. In simple terms, clustering refers to groupings of individuals (or entities) that are tied together by some common attribute (or multiple attributes). This can represent virtually anything, as long as it is effective in defining and differentiating individual clusters. The goal is to identify a common linkage that separates one group from another. This commonality might be based on geography, shopping patterns, age, type of vehicles driven, preferred genres of movies, and so on. It might well be a combination of these attributes that distinguishes clusters from one another. Let's take a look at an example.

Suppose we use the attributes identified earlier, and wind up with the following distinct clusters: