<p>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.</p>

<p>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?</p>

CC BY-SA 3.0
2011-04-20T16:54:30.393Z
this question might be better suited to metaoptimize, but we do have some ML folks here who might be able to help out.
Suresh Venkat
CC BY-SA 3.0
2011-04-20T19:33:00.197Z
Thanks @Suresh- it looks like the QA side of metaoptimize is down right now, but I'll run it past them as well.
Matt Luongo
CC BY-SA 3.0
2011-04-21T23:41:22.247Z
Have you checked out the following, along with other works by Lise Getoor? http://linqs.cs.umd.edu/basilic/web/Publications/2007/bhattacharya:tkdd07/bhattacharya-tkdd.pdf From what I remember she seems to focus on exactly these problems/techniques
Lev Reyzin
CC BY-SA 3.0
2011-04-22T15:38:14.473Z
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.
Matt Luongo
<graph-theory><machine-learning><network-modeling>
Using Graphs/Graph Similarity as Features for a Learner
Matt Luongo
# Using Graphs/Graph Similarity as Features for a Learner

(Asked by: username_0 on 2011-04-20)

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?

**Tags:** graph-theory, machine-learning, network-modeling

> **Comments:**
> **username_1**: this question might be better suited to metaoptimize, but we do have some ML folks here who might be able to help out.  
> **username_0**: Thanks @Suresh- it looks like the QA side of metaoptimize is down right now, but I'll run it past them as well.  
> **username_2**: Have you checked out the following, along with other works by Lise Getoor? http://linqs.cs.umd.edu/basilic/web/Publications/2007/bhattacharya:tkdd07/bhattacharya-tkdd.pdf From what I remember she seems to focus on exactly these problems/techniques  
> **username_0**: 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.  

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