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This is largely a literature request.

I have some data consisting of, for each use of a credit card, the card owner, the store, and the time of the use. This could be viewed as a collection of dependent point processes associated with the edges of a big graph, where the vertices are credit card owners and stores that accept credit cards.

I'm interested in trying to model this information, in particular in such a way that one could try to simulate data from the model afterwards without too much difficulty. It is ok if one can only simulate at small discrete time steps. Is there a literature on this type of topic? Any great papers to read?

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It would be helpful if you could also say something about your goals for the model & subsequent simulations. Eg, are you hoping to predict future use, or understand the conditions under which customers are more likely to use credit cards vs. cash (etc)? In addition, I'm wondering if the machine-learning tag would also be appropriate on this question. – gung Jun 25 '12 at 15:29
Thanks for the comment. I'm really in an exploratory phase right now, but I think the main goals are looking for unusual patterns - either a change in some region, or e.g. people who shop together, or stores that seem to get the same shoppers in quick succession. Of course, all of these are to be expected - we would assume that there is a relationship between where friends shop! – qast Jun 26 '12 at 11:44

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