THESIS
2018
xi, 88 pages : illustrations ; 30 cm
Abstract
Numerous research has been devoted to the study of social network, while
relatively little research is related to business network. Besides, the dynamic
nature and heterogeneous types of edges in network still remain under-researched.
In this paper, an advanced statistical network model is proposed
and extended to analyze dynamic and multi-view company networks. Multi-view
refers to heterogeneous tyeps of edges among same set of nodes. The
statistical model assumes that the probability of link between a pair of nodes
depends only on the underlying unobserved space. Therefore, nodes that are
close to each other in the unobserved space are more likely to have link. The
statistical model is inferenced within the Bayesian framework, and the parameters
are estimated using Markov Ch...[
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Numerous research has been devoted to the study of social network, while
relatively little research is related to business network. Besides, the dynamic
nature and heterogeneous types of edges in network still remain under-researched.
In this paper, an advanced statistical network model is proposed
and extended to analyze dynamic and multi-view company networks. Multi-view
refers to heterogeneous tyeps of edges among same set of nodes. The
statistical model assumes that the probability of link between a pair of nodes
depends only on the underlying unobserved space. Therefore, nodes that are
close to each other in the unobserved space are more likely to have link. The
statistical model is inferenced within the Bayesian framework, and the parameters
are estimated using Markov Chain Monte Carlo (MCMC) procedures.
We demonstrate the empirical value of our model by applying it to two company
networks, the investment network and the news network, with same
set of nodes. The investment network is constructed from investment transactions
collected from the Thomson Reuters Eikon while the news network
is constructed from financial news collected from the Reuters site archive.
We show that our model can be applied in many business applications such
as measuring business proximity, studying business influence, understanding
alliance structure and predicting business relationship.
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