Graphs in the world: Modeling systems as networks by Russel Jurney.
From the post:
Networks of all kinds drive the modern world. You can build a network from nearly any kind of data set, which is probably why network structures characterize some aspects of most phenomenon. And yet, many people can’t see the networks underlying different systems. In this post, we’re going to survey a series of networks that model different systems in order to understand different ways networks help us understand the world around us.
We’ll explore how to see, extract, and create value with networks. We’ll look at four examples where I used networks to model different phenomenon, starting with startup ecosystems and ending in network-driven marketing.
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Loaded with successful graph modeling stories Russel’s post will make you anxious to find a data set to model as a graph.
Which is a good thing.
Combining two inboxes (Russel’s and his brother’s) works because you can presume that identical email addresses belong to the same user. But what about different email addresses that belong to the same user?
For data points that will become nodes in your graph, what “properties” do you see in them that make them separate nodes? Have you captured those properties on those nodes? Ditto for relationships that will become arcs in your graph.
How easy is it for someone other than yourself to combine a graph you make with a graph made by a third person?
Data, whether represented as a graph or not, is nearly always “transparent” to its creator. Beyond modeling, the question for graphs is have you enabled transparency for others?
I first saw this in a tweet by Kirk Borne.