As a natural extension of link prediction on graphs, hyperlink prediction aims for the
inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than …

Hypergraphs have shown great power in representing high-order relations among entities,
and lots of hypergraph-based deep learning methods have been proposed to learn …

Collective user behavior in social media applications often drives several important online
and offline phenomena linked to the spread of opinions and information. Several studies …

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 …

Complex networked systems often exhibit higher-order interactions, beyond dyadic
interactions, which can dramatically alter their observed behavior. Consequently …

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

Link prediction is a significant research problem in network science and has widespread
applications. To date, much efforts have focused on predicting the links generated by …

It is demonstrated that higher-order patterns beyond pairwise relations can significantly
enhance the learning capability of existing graph-based models, and simplex is one of the …

Link prediction in graphs is studied by modeling the dyadic interactions among two nodes.
The relationships can be more complex than simple dyadic interactions and could require …

Hyperedges, as extensions of pairwise edges, can characterize higher-order relations
among multiple individuals. Due to the necessity of hypergraph detection in practical …