Random graph null models have found widespread application in diverse research
communities analyzing network datasets, including social, information, and economic …

Simplicial complexes are now a popular alternative to networks when it comes to describing
the structure of complex systems, primarily because they encode multinode interactions …

Network science is a powerful tool for analyzing complex systems in fields ranging from
sociology to engineering to biology. This article is focused on generative models of large …

Hypergraphs offer a natural modeling language for studying polyadic interactions between
sets of entities. Many polyadic interactions are asymmetric, with nodes playing distinctive …

The problem of efficiently sampling from a set of (undirected) graphs with a given degree
sequence has many applications. One approach to this problem uses a simple Markov …

There is a well-known connection between hypergraphs and bipartite graphs, obtained by
treating the incidence matrix of the hypergraph as the biadjacency matrix of a bipartite …

The problem of efficiently sampling from a set of (undirected, or directed) graphs with a given
degree sequence has many applications. One approach to this problem uses a simple …

To understand why and how species invade ecosystems, ecologists have made heavy use
of observations of species colonization on islands. The theory of island biogeography …

Many real-world networks exhibit correlations between the node degrees. For instance, in
social networks nodes tend to connect to nodes of similar degree and conversely, in …

Since 1997 a considerable effort has been spent to study the mixing time of switch Markov
chains on the realizations of graphic degree sequences of simple graphs. Several results …