Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

Kernel methods and deep learning are two of the most currently remarkable machine
learning techniques that have achieved great success in many applications. Kernel methods …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly develo** branch of learning on structured data. During the past 20 years, the …

The increasing digitization and datification of all aspects of people's daily life, and the
consequent growth in the use of personal data, are increasingly challenging the current …

Graph-structured data are an integral part of many application domains, including
chemoinformatics, computational biology, neuroimaging, and social network analysis. Over …

Several machine learning problems can be naturally defined over graph data. Recently,
many researchers have been focusing on the definition of neural networks for graphs. The …

Deep neural networks and Multiple Kernel Learning are representation learning
methodologies of widespread use and increasing success. While the former aims at learning …

Since all training data is interpolated, interpolating classifiers have zero training error.
However, recent work provides compelling reasons to investigate these classifiers, including …

Graph, a kind of structured data, is widely used to model complex relationships among
objects, and has been used in various of scientific and engineering fields, such as …

The graph embedding is the process of representing the graph in a vector space using
properties of the graphs and this technique has now being widely used for analyzing the …