Influence maximization (IM) has shown wide applicability in immense fields over the past
decades. Previous researches on IM mainly focused on the dyadic relationship but lacked …

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide
a promising way to model higher-order relations in data and further solve relevant prediction …

Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications, and thus investigation of deep learning for HOIs has become a valuable …

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an
appropriate representation for modeling polyadic relations in complex systems. With the …

Hypergraphs (ie, sets of hyperedges) naturally represent group relations (eg, researchers co-
authoring a paper and ingredients used together in a recipe), each of which corresponds 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 …

Group interactions arise in various scenarios in real-world systems: collaborations of
researchers, co-purchases of products, and discussions in online Q&A sites, to name a few …

Hypergraphs have emerged as a powerful modeling framework to represent systems with
multiway interactions, that is systems where interactions may involve an arbitrary number of …

Hypergraphs are a natural and powerful choice for modeling group interactions in the real
world, which are often referred to as higher-order networks. For example, when modeling …

Graphs are widely used for representing pairwise interactions in complex systems. Since
such real-world graphs are large and often evergrowing, sampling a small representative …