Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Network neuroscience is the emerging discipline concerned with investigating the complex
patterns of interconnections found in neural systems, and identifying principles with which to …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is
proposed. The probability model assumes that the vertices of the digraph are partitioned into …

The paper explores DiMaggio and Powell's thesis that under conditions of uncertainty
organizational decision makers will mimic the behavior of other organizations in their …

a posteriori blockmodeling for graphs is proposed. The model assumes that the vertices of
the graph are partitioned into two unknown blocks and that the probability of an edge …

Das in zweiter Auflage erscheinende Lehrbuch vermittelt dem Leser die Grundlagen und
Methoden der Netzwerkanalyse. Die statistischen Instrumente zur Erhebung und Analyse …

This paper explores the opportunities for the application of network analytic techniques to
the problems of criminal intelligence analysis, paying particular attention to the identification …

Network models are widely used to represent relations between interacting units or actors.
Network data often exhibit transitivity, meaning that two actors that have ties to a third actor …

Interest in log-linear modeling for social-network data has grown steadily since Holland and
Leinhardt (1981) proposed their p 1 model. That model was designed for a single binary …