Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Message passing Graph Neural Networks (GNNs) provide a powerful modeling
framework for relational data. However, the expressive power of existing GNNs is upper …

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …

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 …

While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their
practical performances are limited by their expressive power, as the kernel function often …

Node-level random walk has been widely used to improve Graph Neural Networks.
However, there is limited attention to random walk on edge and, more generally, on $ k …

We introduce SignNet and BasisNet--new neural architectures that are invariant to two key
symmetries displayed by eigenvectors:(i) sign flips, since if $ v $ is an eigenvector then so is …

In recent years, graph neural networks (GNNs) have become the de facto tool for performing
machine learning tasks on graphs. Most GNNs belong to the family of message passing …