Estimating a graph from triad counts
Journal of Statistical Computation and SimulationPublished 1 April 1979
Ove Frank
Citations22
SJR quartileQ2
SJR score0.55
SNIP1.12
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TL;DR
Methods of estimating a transitive graph and a forest are described, and a possible approach for a general graph G is indicated.
Abstract
An empirical graph [Ggrave] is described by a random graph model which generates [Ggrave] from an unknown graph G by independent removals and additions of edges. We consider the problem of estimating G by using the triad counts in [Ggrave], i.e. the numbers of different induced subgraphs of order three in [Ggrave]. We describe methods of estimating a transitive graph and a forest, and we indicate a possible approach for a general graph G.
Keywords
MathematicsPhysics and Astronomy
Sociological MethodologyLocal Structure in Social Networks
431 Citations1976Paul W. Holland, Samuel Leinhardt
This chapter was written when Paul Holland was with the Computer Research Center for Economics and Management Science of the National Bureau of Economic Research, Inc.
Social NetworksSampling and estimation in large social networks
130 Citations1978Ove Frank
Unbiased estimators and variance estimators of such graph parameters which can be given as dyad or triad counts are found and approximate formulae pertaining to large networks are given.
Journal of Mathematical SociologyRandom directed graph distributions and the triad census in social networks†
68 Citations1977Stanley Wasserman
Methods are presented for calculating the mean and the covariance matrix of the triad census for the uniform distribution that conditions on the number of choices made by each individual in the social network.
Estimation of the Number of Connected Components in a Graph by Using a Sampled Subgraph
42 Citations1978Ove Frank
The number of connected components in an unknown parent graph is to be estimated by using a sampled subgraph using a transitive graph and a forest.
Annals of the New York Academy of SciencesMOMENT PROPERTIES OF SUBGRAPH COUNTS IN STOCHASTIC GRAPHS
26 Citations1979Ove Frank