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An Introduction to Graph Kernels (Sandro Vega Pons)
Graph-structured data is becoming more and more abundant in many fields of science and engineering, such as social network analysis, molecular biology, chemistry, computer vision, etc. Machine learning methods that are able to efficiently handlegraph data sets are needed to exploit this kind of data. Successfully application of machine learning and data analysis methods to graphs requires the ability to efficiently compare graphs. Graph kernels have attracted considerable interest in the machine learning community in the last decade as a promising solution to this issue.This talk is an introduction to graph kernels, we will review the state-of-the-art, presenting the main strategies for defininggraph kernels and making an analysis of the expressivity and computational complexity. We will also present some experiments reported in the literature and some available codes.