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The social graph is a graph that represents social relations between entities. In short, it is a model or representation of a social network, where the word graph has been taken from graph theory. The social graph has been referred to as "the global mapping of everybody and how they're related". [1]
Social network analysis within the versatile and popular R environment R will read in almost any format data file R has write capability for most data formats Windows, Linux, Mac Open source: R contains several packages relevant for social network analysis: igraph is a library collection for creating and manipulating graphs and analyzing ...
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. [1] It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties , edges , or links (relationships or interactions) that connect them.
Social visualization is a subset of information visualization. According to Karrie G. Karahalios and Fernanda Viégas, one of the most distinctive aspect of social visualization is that "social visualization focuses on people, the groups they form, their patterns, their interactions, and how they related to their communities." rather than other ...
Visualization of Simpson's paradox on data resembling real-world variability indicates that risk of misjudgment of true causal relationship can be hard to spot. Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.
Under the social discipline model, sociograms are sometimes used to reduce misbehavior in a classroom environment. [4] A sociogram is constructed after students answer a series of questions probing for affiliations with other classmates. The diagram can then be used to identify pathways for social acceptance for misbehaving students.
A certain category of small-world networks were identified as a class of random graphs by Duncan Watts and Steven Strogatz in 1998. [4] They noted that graphs could be classified according to two independent structural features, namely the clustering coefficient, and average node-to-node distance (also known as average shortest path length).
Interactive data visualization enables direct actions on a graphical plot to change elements and link between multiple plots. [59] Interactive data visualization has been a pursuit of statisticians since the late 1960s. Examples of the developments can be found on the American Statistical Association video lending library. [60] Common ...