Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major
step to better describe complex systems. In the resulting hypergraph representation, tools to …

Influence maximization (IM) is a key issue in network science. However, previous research
on IM has previously explored binary interaction relationship in ordinary graphs, with little …

Multi-individual interactions are ubiquitous in the real world, which are usually modeled by
hypergraphs. Similar to low-order networks, there are special key nodes in high-order …

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

A recent trend in data mining has explored (hyper) graph clustering algorithms for data with
categorical relationship types. Such algorithms have applications in the analysis of social, co …

We study the approximability of an existing framework for clustering edge-colored
hypergraphs, which is closely related to chromatic correlation clustering and is motivated by …

Compared with general complex networks, the hypernetworks with hypergraphs as the
underlying topological structures facilitate a more detailed description of the characteristics …

Hypergraphs have become an accurate and natural expression of high-order coupling
relationships in complex systems. However, applying high-order information from networks …

Influence maximization in online social networks is used to select a set of influential seed
nodes to maximize the influence spread under a given diffusion model. However, most …

Influence maximization (IM) algorithms play a significant role in hypergraph analysis tasks,
such as epidemic control analysis, viral marketing, and social influence analysis, and …