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Stochastic dynamical model of a growing citation network based on a self-exciting point process
Michael Golosovsky1, Sorin Solomon
1The Racah Institute of Physics, The Hebrew University of Jerusalem, 91904 Jerusalem, Israel. michael.golosovsky@mail.huji.ac.il
Physical Review Letters
|September 26, 2012
Summary
This study challenges the preferential attachment model for complex networks. Analysis of physics paper citations reveals superlinear attachment and memory effects, not a simple Markov chain, leading to a new network growth model.
Area of Science:
- Complex networks
- Network science
- Bibliometrics
Background:
- The preferential attachment model is widely accepted for generating scale-free complex networks.
- Citation networks are a key example of complex networks where understanding growth dynamics is crucial.
Purpose of the Study:
- To experimentally scrutinize the preferential attachment model in citation networks.
- To investigate the citation dynamics of individual papers and identify deviations from existing models.
- To develop and validate a new stochastic growth model for citation networks.
Main Methods:
- Analysis of a citation network comprising 40,195 physics papers published in a single year.
- Tracing the citation history of individual papers.
- Developing a novel stochastic growth model based on empirical findings.
- Performing numerical simulations of the proposed model.
Main Results:
- Citation dynamics follow superlinear preferential attachment with an exponent α=1.25-1.3, contrary to common assumptions.
- The citation process exhibits significant correlations between present and recent citation rates, indicating memory effects.
- The proposed stochastic growth model shows excellent agreement with measured citation distributions.
Conclusions:
- The preferential attachment model, in its simplest form, is insufficient to describe citation network evolution.
- Citation networks display memory effects, suggesting a departure from memoryless Markov chain processes.
- A new stochastic growth model incorporating superlinear attachment and memory effects accurately represents citation network dynamics.
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