Dr. Mason Porter gave a very nice and thorough seminar on applications of his method of detecting communities in multi-slice networks. Applications ranged from financial networks to actual human brains (and everything in between). The technique itself is deceptively simply: one simply seeks the community partition that optimizes a well-known network quantity called modularity (where modularity is measured in terms of departure from some chosen null model). However, actually implementing the technique can be extremely challenging.
I am interested in applying these techniques to international trade (which to Dr. Porter's knowledge has not yet been attempted). To start, what I need is to find an appropriate null model for international trade networks. I will then find a community partition that maximizes modularity relative to what one would expect given my null model. Typically, null models are random network models...so which random network model is most descriptive on the international trade network? What about Kronecker graphs?
It's not a multi-slice network, but you should definitely look at J. Reichardt and D. R. White, "Role models for Complex Networks", European Physical Journal B 60 (2007): 217--224, arxiv:0708.0958.
ReplyDeleteThanks for the recommendation. By the way, I am kicking myself for missing your CABDyN presentation earlier this year...
ReplyDeletePostscript: I have more information now about applying multislice to things related to international trade. (Actually, I had completely forgotten about this project, which I had heard about a few months ago.) I will e-mail you. I can't say more in public. :)
ReplyDeleteThanks! Looks very interesting...
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