Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

We propose a new perspective on designing powerful Graph Neural Networks (GNNs). In a
nutshell, this enables a general solution to inject structural properties of graphs into a …

Understanding generalization and robustness of machine learning models fundamentally
relies on assuming an appropriate metric on the data space. Identifying such a metric is …

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

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

While message-passing graph neural networks have clear limitations in approximating
permutation-equivariant functions over graphs or general relational data, more expressive …

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 …