The core of network analysis is picking the correct entities and relationships.
If you have data about co-authorship and institutional affiliation (location) you could create many different kinds of networks.
Networks come in types depending on the different types of entities and relationships captured.
If all the entities are one type of thing, say people, then the network is a "unimodal" network. If you have two kinds of entities (ex: people and papers) it becomes a bimodal network. Three or more (ex: people, papers, places) and
it is multimodal.
If all the connections among the entities are of the same type (ex: follows, co-authors, etc.), the network is a uniplex network. If there are multiple ways the entities can connect (ex: follow, reply mention) then it is a multiplex network.
Both dimensions can be varied (ex: unimodal/multiplex, multimodal/uniplex).
Your job as network analyst is first and foremost to define the things that get into the population of vertices and the ways they can connect.
So, you could create:
Author <> Author (co-citation, unimodal, uniplex, network)
Author <> Paper (bimodal, uniplex)
Paper <> Paper (co-author, unimodal, uniplex, network)
Author <> Location/Institution (bimodal, uniplex)