1987 StochasticBlockmodelsforDirecte

(Wang & Wong, 1987) ⇒ Yuchung J. Wang, and George Y. Wong. (1987). “Stochastic Blockmodels for Directed Graphs.” In: Journal of the American Statistical Association, 82(397).

Subject Headings: Stochastic Partitioned Directed Graph; Stochastic Blockmodeling; Iterative Scaling Algorithm, Structural Relatedness.

Notes

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http://scholar.google.com/scholar?q=%221987%22+Stochastic+Blockmodels+for+Directed+Graphs

Quotes

Author Keywords

Stochastic partitioned directed graph; Blockmodeling technique; Iterative scaling algorithm

Abstract

Holland and Leinhardt (1981) proposed the [math]\displaystyle{ p_1 }[/math] model for the analysis of binary directed graph data in network studies. Such a model provides information about the “attractiveness and “expansiveness of the individual nodes in the network, as well as the tendency of a pair of nodes to reciprocate relational ties. When the nodes are a priori partitioned into subgroups based on attributes such as race and sex, the density of ties from one subgroup to another can differ considerably from that relating another pair of subgroups, thus creating a situation called blocking in social networks. The [math]\displaystyle{ p_1 }[/math] model completely ignores this extra piece of information and is, therefore, unable to explain the block structure. Blockmodels that are simple extensions of the [math]\displaystyle{ p_1 }[/math] model are proposed specifically for such data. An iterative scaling algorithm is presented for fitting the model parameters by maximum likelihood. The methodology is illustrated in detail on two empirical examples.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1987 StochasticBlockmodelsforDirecte	Yuchung J. Wang George Y. Wong			Stochastic Blockmodels for Directed Graphs						1987