GTED: Graph Traversal Edit Distance

Ali Ebrahimpour Boroojeny, Akash Shrestha, Ali Sharifi-Zarchi, Suzanne Renick Gallagher, Süleyman Cenk Sahinalp, Hamidreza Chitsaz

Abstract: 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.