PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Dublin Core
Title
PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Subject
temporal networks, dynamic networks, preferential attachment, fitness, rich-getricher, fit-get-richer, R, C++
Description
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
scientist
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
scientist
Creator
Thong Pham
Source
https://www.jstatsoft.org/article/view/v092i03
Publisher
RIKEN Center for AIP
Date
February 2020
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Thong Pham, “PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8107.