Social network analysis: A methodological introduction

By Carter T. Butts. 2008

500 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/96390ecc1d80d77470936380319c263653888fe0

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, background): “The graph theory approach predominantly deals with the study of nodes and edges, and here the relation between the nodes and edges can be established as G1⁄4 (V, E) where V is a gathering of nodes connected by edges E, which describes the interaction between nodes.(37) The application of graph theor”
Understanding individual behaviors within covert networks: the interplay of individual qualities, psychological predispositions, and network effects (2009, background): “We frequently see careful technical descriptions involving important graph-theory measures: for instance, degree distributions and geodesic lengths, and at a more localized level, the centrality of the actors or the density of ego-networks (for a recent review of these and other network analytic con”
State of the aRt personality research: A tutorial on network analysis of personality data in R (2015, unknown): “Some of the functions rely on procedures originally implemented in packages igraph (Csárdi & Nepusz, 2006), sna (Butts, 2008b), and WGCNA (Langfelder & Horvath, 2008, 2012).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “In addition to being independent, the nodes should be internally coherent or homogeneous, i.e., nodes should be encapsulated informational components that have internal integrity and external independence (Butts 2008, 2009; Rubinov & Sporns 2010).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, methodology): “We briefly sketch four methods that have been considered useful when comparing networks, although, strictly speaking, all these methods are also not entirely insensitive to differences in N and k (see [56,58,59], and references therein).”