Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Graph data are ubiquitous in the real world. Graph learning (GL) attempts to mine and
analyze graph data so that valuable information can be discovered. Existing GL methods are …

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-
order interactions in complex systems. Representation learning for hypergraphs is essential …

Graph convolutional networks (GCNs) have achieved great success in many applications
and have caught significant attention in both academic and industrial domains. However …

Practical real-world scenarios such as the Internet, social networks, and biological networks
present the challenges of data scarcity and complex correlations, which limit the applications …

Functional module recombination is a common means for manufacturers to upgrade and
launch new product quickly. However, this method may bring with significant risks and it …

Network dismantling aims to degrade the connectivity of a network by removing an optimal
set of nodes. It has been widely adopted in many real-world applications such as epidemic …

Influence Maximization (IM) in graphs tries to identify a subset of influential nodes that
maximize the influence spread under a propagation model. Existing works on IM mainly …

Recently, graph convolutional networks (GCNs) have been applied to heterogeneous
information network (HIN) learning and have shown promising performance. However, the …

A hypergraph is a generalization of a graph that depicts higher-order relations. Predicting
higher-order relations, ie hyperedges, is a fundamental problem in hypergraph studies, and …