Data-driven modeling of collaboration networks: a cross-domain analysis

EPJ Data Science

Table 1 Number of nodes N , links L , collaboration events E and average degree \(\pmb{\left \langle k \right \rangle ^{\mathrm{OBS}}=2L/N}\) for the aggregated R&D network, the six largest sectoral R&D networks, and the six representative co-authorship networks

	N	L	Events	\(\boldsymbol{\left \langle k \right \rangle ^{\mathrm{OBS}}}\)
Aggregated R&D network	14,561	21,572	14,829	2.74
Sectoral R&D networks
Pharmaceuticals (SIC 283)	3,829	6,019	5,277	3.14
Computer hardware (SIC 357)	1,582	4,047	2,672	5.12
Communications equipment (SIC 366)	1,133	2,726	1,888	4.81
Electronic components (SIC 367)	1,615	3,756	2,574	4.65
Computer software (SIC 737)	3,381	5,862	4,134	3.47
R&D, laboratory and testing (SIC 873)	3,188	5,364	4,032	3.37
Co-authorship networks
Quant. mech., field theories, spec. relativity (PACS 03)	21,501	56,111	19,647	5.22
General relativity and gravitation (PACS 04)	8,294	32,513	8,158	7.84
Optics (PACS 42)	27,436	94,961	20,105	6.92
Electronic transport in condensed matter (PACS 72)	19,492	55,818	11,687	5.73
Superconductivity (PACS 74)	14,920	52,615	10,541	7.05
Other applied and interdisciplinary physics (PACS 89)	4,881	8,777	2,873	3.60

For all domains, we consider the respective cumulative networks, i.e. the networks obtained by keeping all the links at any time.