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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Horst Bunke, Michel Neuhaus
• Keywords: Computer Vision, Edit Distance Computation, Edit Operation Costs, Graph Edit Distance, ICPR 2004