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Summary
Centrality in random
graphs is studied in two reports: Testing Centrality in Random Graphs
and Comparing Degree-based and Closeness-based Centrality Measures.
By using block models, centrality is tested in random graphs. The null
hypothesis model assumed to generate edges with common probability is
the Bernoulli(p) -distribution. First an attempt is made to derive the
maximum likelihood estimators of the parameters in the block model. It
is shown that they are troublesome to derive. Therefore centrality testing
is performed by computer simulation. Properties of the tests are investigated.
Actor level centrality indices are aggregated to obtain graph centrality
indices. Ten tests based on graph level centralities are presented. Two
of the tests are based on degree and eight of the tests are based on closeness.
It is concluded that none of the tests are uniformly most powerful. A
general tendency is that the heterogeneity- based tests generate stronger
power than the average-based tests. Noted is that also the tests defined
as the maximums of the actor centralities have strong power.
Under the assumptions that the edges in realizations of directed random
graphs are conditionally independent Bernoulli(pi)-distributed, where
the edge probabilities, pi, are independently beta-distributed, statistical
properties of four graph centrality measures are investigated. Three of
the measures are extensively used within the context of social networks;
the maximum of the actor centralities, the mean centrality and the variance
of the centralities. The fourth measure considered is the difference between
the maximum of the actor centralities and the mean centrality. The graph
centrality measures are investigated for two different actor centrality
concepts, degree centrality and closeness centrality. Analytical results
of statistical properties of the measures for degree-based centrality
are derived, while similar results for closeness-based measures are difficult
to derive. Statistical properties are obtained for both degree-based measures
and closeness-based measures by computer simulation, where the empirical
results agree with the theoretically derived results for the degree-based
measures.
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