Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all
subgraphs that appear frequently in a database according to a given frequency threshold …

Graph kernels based on the $1 $-dimensional Weisfeiler-Leman algorithm and
corresponding neural architectures recently emerged as powerful tools for (supervised) …

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 …

Graph mining is an important research area within the domain of data mining. The field of
study concentrates on the identification of frequent subgraphs within graph data sets. The …

In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …

This paper considers the scheduling of parallel real-time tasks with implicit deadlines. Each
parallel task is characterized as a general directed acyclic graph (DAG). We analyze three …

Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …

Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …