The association of network topology with dissimilarities of indices is assessed
Goodness of fit tests for different probability models for random multigraphs.
A method for performing multiplexity analysis in social networks with several node covariates is presented.
A review chapter on social network analysis aimed towards undergraduate students.
We present a general framework in which we combine exponential random graph models with archaeological substantiations of mechanisms for network formation.
We consider exponential random graph models, and show how they can be applied to reconstruct networks coherent with Burt’s arguments on closure and structural holes.
New combinatorial results are given for the global probability distribution of edge multiplicities and its marginal local distributions of loops and edges.
We perform a centrality analysis of a directed hypergraph representing attacks by indigenous peoples from the Lesser Antilles on European colonial settlements, 1509-1700.
We develop and test more standardized and quantitative approaches to geographic assignment of individual origins using multivariate isotopic data.
The local and global structures of undirected multigraphs under two random multigraph models are analyzed and compared.
Social network analysis is used to demonstrates the signaling practices reflecting regional patterns.
We show how it is possible to systematically check for tendencies in data, such as independencies or conditional independencies, using multivariate entropies.
We consider different approaches for data privacy in online social networks and for developing graph protection.
The theoretical background for analyzing multivariate social networks using multigraph representations is introduced.
Complexity measured for multigraphs are specified and their applicability is discussed.
