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Journal ArticleDOI

An Exponential Family of Probability Distributions for Directed Graphs

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TLDR
An exponential family of distributions that can be used for analyzing directed graph data is described, and several special cases are discussed along with some possible substantive interpretations.
Abstract
Directed graph (or digraph) data arise in many fields, especially in contemporary research on structures of social relationships. We describe an exponential family of distributions that can be used for analyzing such data. A substantive rationale for the general model is presented, and several special cases are discussed along with some possible substantive interpretations. A computational algorithm based on iterative scaling procedures for use in fitting data is described, as are the results of a pilot simulation study. An example using previously reported empirical data is worked out in detail. An extension to multiple relationship data is discussed briefly.

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Journal ArticleDOI

The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI

Stochastic blockmodels: First steps

TL;DR: Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori, and an extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition.
Journal Article

Research methods in social relations

A. R. Ilersic
- 01 Jan 1961 - 
TL;DR: This sales letter may not influence you to be smarter, but the book that this research methods in social relations will evoke you to being smarter.
Journal ArticleDOI

Latent Space Approaches to Social Network Analysis

TL;DR: This work develops a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space,” and proposes Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates.
References
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Journal ArticleDOI

Centrality in social networks conceptual clarification

TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
Journal ArticleDOI

A Set of Measures of Centrality Based on Betweenness

TL;DR: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced in this paper, which define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication.
Book

Testing statistical hypotheses

TL;DR: The general decision problem, the Probability Background, Uniformly Most Powerful Tests, Unbiasedness, Theory and First Applications, and UNbiasedness: Applications to Normal Distributions, Invariance, Linear Hypotheses as discussed by the authors.
Book

analysis of binary data

David Cox, +1 more
TL;DR: Binary response variables special logistical analyses some complications some related approaches more complex responses.
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