Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks

  title={Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks},
  author={Masaaki Inoue and Thong Pham and Hidetoshi Shimodaira},
Exploring Mathematical Scholarship through the Mathematics Collaboration Graph
we The column below is written and describes the results from a project he did on the collaboration graph inher-ent in our database of authors. His work updates and ex-pands upon the work of others,


PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks
PAFit constitutes an advance over previous methods primarily because it based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form, and it is found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form.
PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
The R package PAFit is introduced, 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.
Joint estimation of preferential attachment and node fitness in growing complex networks
This work introduces a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms.
Measuring preferential attachment for evolving networks
Measurements on four networks indicate that the rate at which nodes acquire links depends on the node's degree, offering direct quantitative support for the presence of preferential attachment.
The Rich get Richer and the Fit get Richer Phenomena in Temporal Complex Networks in the Strategic Management Scientific Community
The results suggest the co-authorship and citation temporal networks are governed by both the fit get richer and the rich get richer processes.
The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal
It was found that on average, while the veterans tend to be more competent at developing new collaborations, the newcomers are likely better at acquiring new citations and coupling node fitnesses throughout different networks might be a promising new direction in analyzing simultaneously multiple networks.
Temporal effects in the growth of networks
It is shown that to explain the growth of the citation network by preferential attachment, one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time, which makes the model an apt candidate for modeling a wide range of real systems.
Modes of collaboration in modern science: Beyond power laws and preferential attachment
Three collaboration modes that correspond to three distinct ranges in the distribution of collaborators are found, and it is found that authors with between 250 and 800 collaborators are more frequent than expected because of the hyperauthorship practices in certain subfields.
Scientific collaboration dynamics in a national scientific system
The collaboration structures and dynamics of the co-authorship network of all Slovenian researchers are examined to identify the key factors driving collaboration and the main differences in collaboration behavior across scientific fields and disciplines.