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A Probabilistic Approach to Learning Costs for Graph Edit Distance

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A Probabilistic Approach to Learning Costs for Graph Edit Distance
Graph edit distance provides an error-tolerant way to measure distances between attributed graphs. The effectiveness of edit distance based graph classification algorithms relies on the adequate definition of edit operation costs. We propose a cost inference method that is based on a distribution estimation of edit operations. For this purpose we employ an Expectation Maximization algorithm to learn mixture densities from a labeled sample of graphs and derive edit costs that are subsequently applied in the context of a graph edit distance computation framework. We evaluate the performance of the proposed distance model in comparison to another recently introduced learning model for edit costs.
Horst Bunke, Michel Neuhaus
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Horst Bunke, Michel Neuhaus
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