I'm working to construct a learner than can recognize whether two vertices in a property graph (digraph, vertices and edges can have arbitrary keys/values) modelling a social network in fact represent the same person. I want to use the structure of the graph local to two vertices, compare those structures, and use the similarity as a feature.

I've had trouble finding related work. Light review of the literature leaves me stuck on graph methods - eg, Bayesian networks and graphs-as-learners- as opposed to learning graph-based domains. Are there any good resources I'm missing, or maybe suggested methods?

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    $\begingroup$ this question might be better suited to metaoptimize, but we do have some ML folks here who might be able to help out. $\endgroup$ – Suresh Venkat Apr 20 '11 at 16:54
  • $\begingroup$ Thanks @Suresh- it looks like the QA side of metaoptimize is down right now, but I'll run it past them as well. $\endgroup$ – Matt Luongo Apr 20 '11 at 19:33
  • $\begingroup$ Have you checked out the following, along with other works by Lise Getoor? linqs.cs.umd.edu/basilic/web/Publications/2007/… From what I remember she seems to focus on exactly these problems/techniques $\endgroup$ – Lev Reyzin Apr 21 '11 at 23:41
  • $\begingroup$ Thanks @Lev, this looks like a great lead. I'd been viewing the problem from the name disambiguation angle, but entity resolution is probably more appropriate. $\endgroup$ – Matt Luongo Apr 22 '11 at 15:38

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