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 …

With the advent of big data and the information age, the data magnitude of various complex
networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary …

Graph representation learning for hypergraphs can be used to extract patterns among
higher-order interactions that are critically important in many real world problems. Current …

Graph neural networks (GNNs) have been widely used for graph structure learning and
achieved excellent performance in tasks such as node classification and link prediction …

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 …

Bundle recommendation, which seeks to recommend a group of items to users, is widely
used in real-world applications. Despite the success of current bundle recommendation …

The information perceived via visual observations of real-world phenomena is unstructured
and complex. Computer vision (CV) is the field of research that attempts to make use of that …

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

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 …

Network representation learning has aroused widespread interests in recent years. While
most of the existing methods deal with edges as pairwise relationships, only a few studies …