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

This paper reviews and extends the field of similarity-based classification, presenting new
analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich …

Most graph kernels are an instance of the class of R-Convolution kernels, which measure
the similarity of objects by comparing their substructures. Despite their empirical success …

Convex potential minimisation is the de facto approach to binary classification. However,
Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of …

This paper presents new and effective algorithms for learning kernels. In particular, as
shown by our empirical results, these algorithms consistently outperform the so-called …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

In complex pattern recognition tasks, objects are typically characterized by means of
multimodality attributes, including categorical, numerical, text, image, audio, and even …

This paper examines two-stage techniques for learning kernels based on a notion of
alignment. It presents a number of novel theoretical, algorithmic, and empirical results for …

Abstract Current Graph Neural Networks (GNN) architectures generally rely on two important
components: node features embedding through message passing, and aggregation with a …

Many real-world data mining tasks require the achievement of two distinct goals when
applied to unseen data: first, to induce an accurate preference ranking, and second to give …