Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

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 …

In recent years, graph neural networks (GNNs) have emerged as a powerful neural
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …

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 …

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network
structure in which the node indicates entity and the edge represent relation. However, with …

We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …

Graph similarity search is among the most important graph-based applications, eg finding
the chemical compounds that are most similar to a query compound. Graph …

Attributed graph clustering is challenging as it requires joint modelling of graph structures
and node attributes. Recent progress on graph convolutional networks has proved that …

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 …