Table 2 Model comparison results for growing network representations of the APS citation data at various time resolutions.

From: A Preferential Attachment Paradox: How Preferential Attachment Combines with Growth to Produce Networks with Log-normal In-degree Distributions

Resolution

Model

Attach. Fn.

Eq.

AIC

BIC

Maximal

Krapivsky

Log-linear

(6)

−4,174

−4,294

Redner

Nonlinear

(7)

−15,056

−12,429

Daily

Krapivsky

Log-linear

(6)

−7,262

−7,252

Redner

Nonlinear

(7)

−12,434

−12,423

Monthly

Krapivsky

Log-linear

(6)

−6,548

−6,538

Redner

Nonlinear

(7)

−7,716

−7,706

Yearly

Krapivsky

Log-linear

(6)

−4,207

−4,198

Redner

Nonlinear

(7)

−3,887

−3,878

  1. Shown are AIC and BIC values for the fit of the log-linear attachment function of Krapivsky’s model and the nonlinear one of Redner’s model to the maximally, daily, monthly, and yearly resolved APS citation data attachment rate, respectively. The best model (the smallest AIC/BIC value) for each level of resolution is indicated in bold. Redner’s model best describes the data at the three highest levels of resolution (maximal, daily, and monthly). Krapivsky’s model best describes the data at the lowest level of resolution (yearly).
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