Suppose I have a dataset with 200 observations of 30 categorical variables.

If I were to convert this into a network format (for example, using the igraph package in R), could I analyse it effectively?

More specifically, could I use clustering methods within the broad field of social network analysis (e.g. modularity) instead of, for example, multiple correspondence analysis?

What kinds of fundamental statistical errors might I blunder into using such an approach, if any?

Suppose I have a dataset with 200 observations of 30 categorical variables. The dataset describes websites and different kinds of design features they deploy (or do not deploy).

If I were to convert this into a network format (for example, using the igraph package in R), could I analyse it effectively using social network analysis?

For example, the network could be two-mode: vertices are either observations (websites) or categories of variables. A directed edge between vertex i and vertex j means that website i deployed design feature j.

More specifically, could I use clustering methods (e.g. modularity) instead of, for example, multiple correspondence analysis?

What kinds of fundamental statistical errors might I blunder into using such an approach, if any?