Yeah, that worked great thanks!!

--
Aaron Weiss
Manager - Financial Intelligence Unit
Western Union Global Compliance
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[cid:[email protected]]

On Jan 29, 2015, at 6:28 PM, "Gábor Csárdi" 
<[email protected]<mailto:[email protected]>> wrote:

Are you looking for connected components?
http://en.wikipedia.org/wiki/Connected_component_%28graph_theory%29<https://urldefense.proofpoint.com/v2/url?u=http-3A__en.wikipedia.org_wiki_Connected-5Fcomponent-5F-2528graph-5Ftheory-2529&d=AwMFaQ&c=r8e_2craKrV6D47TmKYPXpXJg_yAmgbggEcE6TnkxiU&r=scvlhDEOhWxp5p0ZmEqoccbaCh7H5QAY30teR_FgVUQ&m=QxBi5ksAsZuEhSAZl1b5MDpxX2grMzQZb8DoNV23iaI&s=jjlXkNM4TMMw28Z2Z5zPYj2nOIkG20ftjq6ymVezAEY&e=>

That is clusters() in igraph.

Gabor

On Wed, Jan 28, 2015 at 12:32 PM, Aaron Weiss 
<[email protected]<mailto:[email protected]>> wrote:
I’m trying to find the right igraph function (or combination of functions) to 
identify multiple, disconnected networks within large transactional datasets 
(where Entity1 is linked to Entity2 via a payment).  I have been playing with 
multilevel.communities, but I’m not sure this function – or any community 
detection function – is the right tool.

I do not want to identify clusters or subgroups with networks; I simply want to 
detect entity membership at the network level, regardless of that network’s 
structure, clustering, etc.  Ideally, I’d like to be able to specify that 
networks with fewer than XXX entities/nodes/vertices be excluded, but that is a 
separate consideration.

Any advice is appreciated.

Best,
Aaron

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