This letter proposes a new model for preferential attachment in dynamic directed networks. This model consists of a linear time-invariant system that uses past observations to predict future attachment rates, and an innovation noise process that induces growth on vertices that previously had no attachments. Analyzing a large citation network in this context, we show that the proposed model fits the data better than existing preferential attachment models. An analysis of the noise in the dataset reveals power-law degree distributions often seen in large networks, and polynomial decay with respect to age in the probability of citing yet-uncited documents.
Bibliographical noteFunding Information:
Manuscript received February 20, 2012; accepted March 22, 2012. Date of publication April 18, 2012; date of current version April 26, 2012. This work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Contract FA8721-05-C-0002. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA or the U.S. Government. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Michael Rabbat.
- Graph theory
- large network analysis
- network growth
- preferential attachment
- stochastic models
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics