Abstract
Graph edit distance measures distances between two graphs \(g_1\) and \(g_2\) by the amount of distortion that is needed to transform \(g_1\) into \(g_2\). The basic distortion operations of graph edit distance can cope with arbitrary labels on both nodes and edges as well as with directed or undirected edges. Therefore, graph edit distance is one of the most flexible dissimilarity models available for graphs. The present chapter gives a formal definition of graph edit distance as well as some basic properties of this distance model. In particular, it presents an overview of how the cost model can be chosen in a certain graph edit distance application. Moreover, the exact computation of graph edit distance based on a tree search algorithm is outlined. In the last section of this chapter, three general approaches for graph edit distance-based pattern recognition are briefly reviewed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
