Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

Recent studies have shown that novel collective behaviors emerge in complex systems due
to the presence of higher-order interactions. However, how the collective behavior of a …

Hypergraphs are a natural modeling paradigm for networked systems with multiway
interactions. A standard task in network analysis is the identification of closely related or …

Network data has become widespread, larger, and more complex over the years. Traditional
network data is dyadic, capturing the relations among pairs of entities. With the need to …

The minimum st cut problem in graphs is one of the most fundamental problems in
combinatorial optimization, and graph cuts underlie algorithms throughout discrete …

Hypergraph-based machine learning methods are now widely recognized as important for
modeling and using higher-order and multiway relationships between data objects. Local …

Dynamic systems that consist of a set of interacting elements can be abstracted as temporal
networks. Recently, higher-order patterns that involve multiple interacting nodes have been …

Influence maximization problem has received significant attention in recent years due to its
application in various domains, such as product recommendation, public opinion …

Submodular functions are at the core of many machine learning and data mining tasks. The
underlying submodular functions for many of these tasks are decomposable, ie, they are …

Label spreading is a general technique for semi-supervised learning with point cloud or
network data, which can be interpreted as a diffusion of labels on a graph. While there are …