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Published in Harry Crane, Probabilistic Foundations of Statistical Network Analysis, 2018
While much of this book is dedicated to modeling single instances of a network, there is emerging interest in analyzing dynamic network data, such as temporal observations of brain activity and social media interactions. But so far statistical work on dynamic networks is mostly confined to theory and applications for the temporal exponential random graph model or other ad hoc approaches. Because network dynamics add another dimension to the already challenging problem of network modeling, the foundations of dynamic network analysis are even more technically and conceptually challenging than their non-dynamic counterpart. Chapter 11, in which I give a brief non-technical overview of some otherwise technical work from the stochastic processes literature [44,48,57], offers a potential starting point for a more general theory of dynamic network modeling. More in depth coverage of dynamic network analysis is beyond the scope of this book and is left as a topic worthy of its own book length treatment.
Analysing dynamic work systems using DynEAST: a demonstration of concept
Published in Ergonomics, 2023
Matt Holman, Guy Walker, Terry Lansdown
Dynamic Network Analysis (DNA) is an emergent scientific field unifying traditional social network analysis (SNA), link analysis, and multi-agent systems theory (Braha and Bar-Yam 2006). Whereas nodes and links in a traditional SNA model (and indeed EAST network) are static, nodes and links within a dynamic network model can have additional properties, such as the ability to evolve over time. Dynamic networks come in several forms and differ from traditional social networks in that they can be;Multiplex: composed of multiple overlapping networks that capture the different types of links between the same nodes throughout a network.Multilayer: a network made up of multiple connected layers each comprising a distinct node set which can be linked via inter-layer edges.Dynamic: a network whose nodes and links can evolve over time (also known as a temporal network) (Newman 2016).