Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, the …

There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for
semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled …

Abstract In Graph Neural Networks (GNNs), hierarchical pooling operators generate local
summaries of the data by coarsening the graph structure and the vertex features …

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …

Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~ 5)
of datasets are currently used to evaluate new models. This continued reliance on a handful …

Graph Neural Networks (GNNs) have drawn considerable attention in recent years and
achieved outstanding performance in many tasks. Most empirical studies of GNNs assume …