Network Growth Modeling to Capture Individual Lexical Learning

Table 1

Network summary statistics of network size (included next to the observed network name and consistent across all random models), network density, mean degree (), measure of transitivity (trans.), mean geodesic distance (geodist.), diameter (diam.), and assortativity coefficients (assort).

Network	Density		Trans.	Geodist.	Diam.	Assort.
McRae (133)	0.191	25.33	0.524	1.99	4	−0.037
McRae CM	0.191 (2e − 3)	25.33 (0.36)	0.309 (5e − 3)	1.86 (0.01)	4.1 (0.31)	0.016 (0.017)
McRae ST	0.187 (1e − 3)	24.81 (0.02)	0.532 (7e − 3)	1.85 (0.01)	3.9 (0.30)	−0.142 (0.011)
Nelson (545)	9.8e − 3	5.38	0.153	4.65	12	0.012
Nelson CM	9.7e − 3 (1e − 4)	5.36 (0.07)	0.027 (1e − 3)	4.04 (0.04)	9.8 (0.78)	−0.004 (0.018)
Nelson ST	9.1e − 3 (8e − 5)	4.99 (0.01)	0.109 (6e − 3)	3.07 (0.05)	6.5 (0.31)	−0.137 (0.013)
Phono (677)	0.956	646.34	0.987	1.04	3	0.013
Phono CM	0.952 (2e − 4)	643.67 (0.12)	0.983 (1e − 4)	1.04 (1e − 4)	3 (0)	−0.004 (7e − 4)

Note. We consider our observed networks, as well as networks constructed via configuration modeling (CM) networks (in which the degree distribution of the observed network is matched), and a preferential attachment algorithm applied to early acquisition (ST) as discussed in Steyvers and Tenenbaum’s 2005 paper [1]. It is easy to see that in most cases, the configuration model cannot capture transitivity or the local structure and no random model can account for the assortativity coefficients.