Determining the closeness of nodes by their neighbours

by James Allen-Robertson   Last Updated January 11, 2019 12:19 PM - source


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.

My 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 event_types.

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.)

Thank you!


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.

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