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

2017/04/30 by Thong Pham, Paul Sheridan, Hidetoshi Shimodaira · 13 citations
Computer Science · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Complex network #Estimation #Function (biology) #Functional Brain Connectivity Studies #Mental Health Research Topics #Node (physics) #Parametric statistics #Preferential attachment #Simple (philosophy) #cs.SI #physics.data-an #physics.soc-ph #stat.CO

paper · pdf · doi:10.18637/jss.v092.i03

published in Journal of Statistical Software 92(3) (Foundation for Open Access Statistics) · Conditionally accepted to Journal of Statistical Software

openalex created_date 2018/05/07 · arxiv created 2018/10/24 · openalex publication_date 2020/01/01 · arxiv updated 2021/03/03 · openalex updated_date 2026/08/06

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

Citations

• Food-web structure and network theory: The role of connectance and size
• Organization of growing random networks
• Emergence of scaling in random networks
• Epidemic Spreading in Scale-Free Networks
• A quasi-Newton acceleration for high-dimensional optimization algorithms
• Bayesian Model Selection in Social Research
• A new look at the statistical model identification
• A Tutorial on MM Algorithms