24,487 research outputs found
Applying relational algebra and RelView to measures in a social network
We present an application of relation algebra to measure agents' 'strength' in a social network with influence between agents. In particular, we deal with power, success, and influence of an agent as measured by the generalized Hoede-Bakker index and its modifications, and by the influence indices. We also apply relation algebra to determine followers of a coalition and the kernel of an influence function. This leads to specifications, which can be executed with the help of the BDD-based tool RelView after a simple translation into the tool's programming language. As an example we consider the present Dutch parliament.RelView; relation algebra; social network; Hoede-Bakker index; influence index
Matrix powers algorithms for trust evaluation in PKI architectures
This paper deals with the evaluation of trust in public-key infrastructures.
Different trust models have been proposed to interconnect the various PKI
components in order to propagate the trust between them. In this paper we
provide a new polynomial algorithm using linear algebra to assess trust
relationships in a network using different trust evaluation schemes. The
advantages are twofold: first the use of matrix computations instead of graph
algorithms provides an optimized computational solution; second, our algorithm
can be used for generic graphs, even in the presence of cycles. Our algorithm
is designed to evaluate the trust using all existing (finite) trust paths
between entities as a preliminary to any exchanges between PKIs. This can give
a precise evaluation of trust, and accelerate for instance cross-certificate
validation
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis
In recent years, ideas from statistics and scientific computing have begun to
interact in increasingly sophisticated and fruitful ways with ideas from
computer science and the theory of algorithms to aid in the development of
improved worst-case algorithms that are useful for large-scale scientific and
Internet data analysis problems. In this chapter, I will describe two recent
examples---one having to do with selecting good columns or features from a (DNA
Single Nucleotide Polymorphism) data matrix, and the other having to do with
selecting good clusters or communities from a data graph (representing a social
or information network)---that drew on ideas from both areas and that may serve
as a model for exploiting complementary algorithmic and statistical
perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors,
"Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201
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