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Title: PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Authors: Pham, Thong
Sheridan, Paul
Shimodaira, Hidetoshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 下平, 英寿
Keywords: temporal networks
dynamic networks
preferential attachment
Issue Date: Feb-2020
Publisher: Foundation for Open Access Statistic
Journal title: Journal of Statistical Software
Volume: 92
Issue: 3
Start page: 1
End page: 30
Abstract: Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential attachment function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential attachment function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential attachment function and node fitnesses in a growing network, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated attachment function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists
Rights: This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License
DOI(Published Version): 10.18637/jss.v092.i03
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