Difference between revisions of "E.A. Leicht, Structure of Time Evo citation networks 2007"
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== '''Brief Description of Three Analysis Methods''' == | == '''Brief Description of Three Analysis Methods''' == | ||
* A mixture model of citation process makes use of [[expectation-maximization algorithm]]. | * A mixture model of citation process makes use of [[expectation-maximization algorithm]]. | ||
| + | Suppose there are '''''n''''' vertices representing documents in a network, it can be divided into '''''c''''' groups. Then a log-likelihood function is given, by maximizing this function, a best estimate of the most likely values of the model parameters can be calculated. This process involves two steps: | ||
| + | 1.estimate the group member probabilities; 2. use the obtained probabilities to maximize the log-likelihood function. Through a few steps mathematical inference and proof, this paper reaches its conclusion the division process by using this model is self-consistent. Some examples are also given as a demonstration of this method. | ||
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* | * | ||
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== '''Related Papers''' == | == '''Related Papers''' == | ||
Revision as of 02:20, 4 February 2011
Contents
Citation
E.A. Leicht, G. Clarkson, K. Shedden, and M.E.J. Newman.2007. Large-scale structure of time evolving citation networks.In European Physical Journal B.-Volume 59, P75–83.
Online Version
Structure of Time Evolving Citation Networks
Summary
This paper uses three methods to examine the structure of large-scale networks (focus in particular on citation networks//link needed) that evolve over time. This paper demonstrates how each of these methods can divide the structure of large-scale network. A network of citations between opinions of the United States Supreme Court is used as an example in this paper.
Brief Description of Three Analysis Methods
- A mixture model of citation process makes use of expectation-maximization algorithm.
Suppose there are n vertices representing documents in a network, it can be divided into c groups. Then a log-likelihood function is given, by maximizing this function, a best estimate of the most likely values of the model parameters can be calculated. This process involves two steps:
1.estimate the group member probabilities; 2. use the obtained probabilities to maximize the log-likelihood function. Through a few steps mathematical inference and proof, this paper reaches its conclusion the division process by using this model is self-consistent. Some examples are also given as a demonstration of this method.
