Table 2.

Network properties at the published and AUTO-TUNE thresholds. In cases when the original paper used more than one threshold, we selected the largest for comparison. The datasets are ordered by the AUTO-TUNE priority score from highest to lowest. ρ is the fitted characteristic scale-free exponent of the corresponding degree distributions.

Reference	AUTO-TUNE score	Nodes in network		Clusters in network		R 12		Scale parameter ρ
		Published	AUTO-TUNE	Published	AUTO-TUNE	Published	AUTO-TUNE	Published	AUTO-TUNE
Li et al. (2022)	2.00	1364	1224	277	277	1.7	2.4	2.8	2.6
Chato et al. (2020) TN	2.00	394	445	108	109	1.0	1.7	2.7	2.9
Rhee et al. (2019)	1.95	2044	1636	524	488	13.2	1.5	2.6	2.7
Bbosa et al. (2020)	1.93	222	296	102	119	2.2	1.6	3.2	2.6
Dalai et al. (2018)	1.89	60	54	9	11	22	2.6	2.0	2.2
Temereanca et al. (2017)	1.79	30	16	5	3	3	1.5	N/A	2.8
Yu et al. (2022)	1.76	55	51	19	19	2.75	1.75	10.4	34.0
Sivay et al. (2018)	1.42	51	51	19	19	1.5	1.5	3.2	3.0
Zai et al. (2020)	1.40	96	98	26	27	1.5	1.5	24.1	17.7
Little et al. (2014)	1.31	301	394	98	87	2.5	6.1	3.6	3.1
Brenner et al. (2021)	1.22	363	379	71	70	5.6	5.5	2.7	2.8
Stecher et al. (2018)	1.20	97	558	36	155	2.2	4.9	3.2	3.3
Chato et al. (2020) Seattle	1.16	505	484	148	149	2.5	1.7	2.7	2.6
Billings et al. (2019)	1.16	38	78	13	23	2	2.3	2.6	11.5
Yan et al. (2021)	1.14	1084	753	124	116	2.0	1.8	1.2	2.0
Chen et al. (2023)	1.11	20	47	8	16	1.3	2.0	∞	∞
Leal et al. (2020)	1.11	50	270	25	57	1	1.6	53.6	3.1
Pérez-Losada et al. (2017)	1.06	172	431	76	134	5.1	1.4	5.2	2.9
Liu et al. (2020)	1.05	885	797	156	161	6.0	4.5	3.1	3.0
Chato et al. (2020) Alberta	1.03	394	445	108	109	1.0	1.7	2.7	2.9
Fabeni et al. (2020)	1.00	626	221	197	83	2.1	3.2	2.1	3.2