Sharif Digital Repository

Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this article, we give a new graph kernel, which we call graph traversal edit distance (GTED). We introduce the GTED problem and give the first polynomial time algorithm for it. Informally, the GTED is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs.... 

Graph Traversal Edit Distance (GTED) is a measure of distance (or dissimilarity) between two graphs introduced. This measure is based on the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs. GTED was motivated by and provides the first mathematical formalism for sequence coassembly and de novo variation detection in bioinformatics. Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space,...