The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

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

Learning representations of sets of nodes in a graph is crucial for applications ranging from
node-role discovery to link prediction and molecule classification. Graph Neural Networks …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …

Recently, graph neural networks have attracted great attention and achieved prominent
performance in various research fields. Most of those algorithms have assumed pairwise …

In many real-world network datasets such as co-authorship, co-citation, email
communication, etc., relationships are complex and go beyond pairwise. Hypergraphs …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

Metro passenger flow prediction is a strategically necessary demand in an intelligent
transportation system to alleviate traffic pressure, coordinate operation schedules, and plan …

Link prediction aims at inferring missing links or predicting future ones based on the
currently observed network. This topic is important for many applications such as social …

Estimation of mutual information from observed samples is a basic primitive in machine
learning, useful in several learning tasks including correlation mining, information …