This modeling approach enables us to explore various aspects of disease spread that happen on individual level.
For example: some individuals meet more people than an average person meets. This makes such individuals super-spreaders.
Another example: a group of people has larger number of mutual interactions than other groups. This group characteristic helps the disease to spread faster within such closely connected cluster.
Social network graphs allow us to test how some epidemiological measure would affect infectious disease spread.
For example the impact of reducing the number of connections between people (social distancing) or exploring the ways a disease can exploit one cohort of people (e.g. young) to reach another (e.g. older people) who are much more at risk of serious outcomes.