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

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

In this article, we propose a family of efficient kernels for large graphs with discrete node
labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler …

We introduce propagation kernels, a general graph-kernel framework for efficiently
measuring the similarity of structured data. Propagation kernels are based on monitoring …

The number of malicious applications targeting the Android system has literally exploded in
recent years. While the security community, well aware of this fact, has proposed several …

Taint-style vulnerabilities are a persistent problem in software development, as the recently
discovered" Heart bleed" vulnerability strikingly illustrates. In this class of vulnerabilities …

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

Negative pairs, especially hard negatives as combined with common negatives (easy to
discriminate), are essential in contrastive learning, which plays a role of avoiding …

Recently a new feature representation and data analysis methodology based on a
topological tool called persistent homology (and its persistence diagram summary) has …

Android platform has dominated the operating system of mobile devices. However, the
dramatic increase of Android malicious applications (malapps) has caused serious software …