I have been working with Social Network Analysis (SNA) methodology for quite some time. I use SNA primarily for quantifying human or social behavior, mostly comparing the dynamic between two social networks, such as in the business or political domain. The methodology also helps me to measure the performance of the power actor in spreading the information, how depth and how wide the flow information coming from him to the rest of the network. Community Detection through Modularity metric, in my opinion, is a powerful tool to unearth the network grouping of actors to texts in the documents.

You can find much of my SNA research on my Google Scholar page

SNA model borrows much of its methodology to Graph Theory and Network Science. So I can imagine the space of SNA scope.

Here I draw my SNA taxonomy version. It includes 5 main parts: Metrics, Network Structures, Community Detection, Temporal Network, Random Walks on Graph, and Visualization. Of course, maybe some other researchers have their own SNA taxonomy version.

From this taxonomy, I have explored or used the tools on the grey bubbles, the rest (white bubbles) still haven't explored yet.