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Learning an efficient constructive sampler for graphs
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DOI：10.1016/j.artint.2016.01.006.png)
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Abstract
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Discriminative systems that can deal with graphs in input are known, however, generative or constructive approaches that can sample graphs from empirical distributions are less developed. Here we propose a Metropolis-Hastings approach that uses a novel type of graph grammar to efficiently learn proposal distributions in a data driven fashion. We report experimental results in a de-novo molecular synthesis problem where we show that the distribution of the molecules generated by the sampling procedure is accurate enough to improve a predictor's performance in a classification task. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Graph construction
Graph sampling
Graph kernels
Metropolis Hastings sampling
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