2017年4月20日

PAFit: an R Package for the Non-parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks

• Thong Pham
• ,
• Paul Sheridan
• ,
• Hidetoshi Shimodaira

DOI
10.1038/srep32558

Many real-world systems are profitably described as complex networks that<br />
grow over time. Preferential attachment and node fitness are two simple growth<br />
mechanisms that not only explain certain structural properties commonly<br />
observed in real-world systems, but are also tied to a number of applications<br />
in modeling and inference. While there are statistical packages for estimating<br />
various parametric forms of the preferential attachment function, there is no<br />
existing package for a non-parametric estimation, which would allow finer<br />
inspections on the famous `rich-get-richer&#039; phenomenon as well as provide clues<br />
to explain non-standard structural properties observed in real-world networks.<br />
This paper introduces the R package PAFit, which implements statistical methods<br />
for estimating the preferential attachment function and node fitness<br />
non-parametrically, as well as a number of functions for generating complex<br />
networks from these two mechanisms. The main computational part of the package<br />
is implemented in C++ with OpenMP to ensure scalability to large-scale<br />
networks. In this paper, we first introduce the main functionalities of PAFit<br />
through simulated examples, and then use the package to analyze a collaboration<br />
network between scientists in the field of complex networks. The results<br />
indicate the joint existence of `rich-get-richer&#039; and `fit-get-richer&#039;<br />
phenomena in the collaboration network. The estimated attachment function is<br />
almost linear, which means that the probability an author develops a new<br />
collaboration is proportional to their current number of collaborators.<br />
Furthermore, the estimated fitnesses reveal many familiar names of the complex<br />
network field as top fittest scientists.

リンク情報
DOI
https://doi.org/10.1038/srep32558
arXiv
http://arxiv.org/abs/arXiv:1704.06017
URL
http://arxiv.org/abs/1704.06017v4
ID情報
• DOI : 10.1038/srep32558
• arXiv ID : arXiv:1704.06017

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