There is increasing evidence that dense networks occur in on-line social networks,
recommendation networks and in the brain. In addition to being dense, these networks are …

We propose an approach to learning with graph-structured data in the problem domain of
graph classification. In particular, we present a novel type of readout operation to aggregate …

Learning on graphs is a subject of great interest due to the abundance of relational data
from real-world systems. Many of these systems involve higher-order interactions (super …

Message-passing has proved to be an effective way to design graph neural networks, as it is
able to leverage both permutation equivariance and an inductive bias towards learning local …

Many processes in biology and drug discovery involve various 3D interactions between
molecules, such as protein and protein, protein and small molecule, etc. Given that different …

Learning and reasoning about 3D molecular structures with varying size is an emerging and
important challenge in machine learning and especially in the development of …

In this paper, we introduce a self-supervised learning method to enhance the graph-level
representations with the help of a set of subgraphs. For this purpose, we propose a universal …

Background Representation learning provides new and powerful graph analytical
approaches and tools for the highly valued data science challenge of mining knowledge …

Contemporary graph learning algorithms are not well-suited for large molecules since they
do not consider the hierarchical interactions among the atoms, which are essential to …

Relations among multiple entities are prevalent in many fields, and hypergraphs are widely
used to represent such group relations. Hence, machine learning on hypergraphs has …