Graph kernels are widely used for measuring the similarity between graphs. Many existing
graph kernels, which focus on local patterns within graphs rather than their global …

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph
kernels focus on local properties of graphs and ignore global structure. In this paper, we …

Graph classification is an important problem in many fields, from bioinformatics and
neuroscience to computer vision and social network analysis. That said, the task of …

Graph-structured data arise in wide applications, such as computer vision, bioinformatics,
and social networks. Quantifying similarities among graphs is a fundamental problem. In this …

Graph Convolutional Networks (GCNs) are powerful representation learning methods for
non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is …

We develop and apply the Balcan-Blum-Srebro (BBS) theory of classification via similarity
functions (which are not necessarily kernels) to the problem of graph classification. First we …

Graph kernels have become a standard approach for tackling the graph similarity and
learning tasks at the same time. Most graph kernels proposed so far are instances of the R …

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool
for graph classification. However, the use of kernel methods for node classification, which is …

Graph classification, which aims to identify the category labels of graphs, plays a significant
role in drug classification, toxicity detection, protein analysis etc. However, the limitation of …

Graph data mining has largely benefited from the recent developments of graph
representation learning. Most attempts to improve graph representations have thus far …