I have a directional network that is comprised of two types of nodes,
business_sector nodes, and
event_type nodes. Currently there are only edges between
business_sector nodes and
event_type nodes and vice versa.
The edges represent a measure of co-occurence between a
business_sector and an 'event_type' in a dataset I have. For every business and event, there are two edges that represent two related measures of co-occurence. The background to these measures is a little involved so I'll leave it out unless someone thinks it pertinent to the question.
Assuming these measures make sense and provide us a good representation of how related different
business_sectors are to different
event_types, is it possible to use this network model to also determine how similar different
business_sectors are based on the relationship each
business_sector has with each
event_type? The business sectors have no edges between them, but share neighbour nodes as all
business_sector nodes connect to all
event_type nodes, with varying weighted edges.
Is it possible to produce a measure that might allow us to say
business_sector a is similar to
business_sector b because they have a similar connectivity to all the
I may not be using the correct vocabulary here but I hope my description is enough. Any input on possible measures or approaches (I'm using Networkx and Gephi but any input on the issue is very welcome.)
I have attempted to measure this outside of graph theory by making every
business_sector an observation, and the weight of each sector to each
event_type a variable and then calculating pairwise cosine distance between each sector, but this doesn't appear to be producing valid results.