Academic collaborations: a recommender framework spanning research interests and network topology

Measuring security development in information technologies: A scientometric framework using arXiv e-prints

“…The empirical literature of these quantitative approaches uses different private-and open-source datasets to measure technological change. Some studies rely on indicators such as bibliometric analyses related to scientific publications (i.e., scientometrics) (Zhang et al, 2020b,a;Mayr et al, 2014;Dotsika and Watkins, 2017;Jaewoo and Woonsun, 2014;Rezaeian et al, 2017), patents (Hajikhani and Suominen, 2022;Choi et al, 2022;Chen et al, 2017;Golembiewski et al, 2015;Noh et al, 2016;An et al, 2018;Lee and Lee, 2019;Song et al, 2017), industrymarket indicators (job openings, trade registers, networks of authors and citations), or a combination of these (An et al, 2022;Xi et al, 2022;Ali et al, 2022;Daim et al, 2012;Lee et al, 2010). To analyze this data, text-mining methods (Chen et al, 2017;Choi and Hwang, 2014;Hao et al, 2014;Antons et al, 2020) and network analysis (Chen et al, 2022;Zhang et al, 2022;Li et al, 2019b;Mikheev, 2020;Feng et al, 2022;Hong et al, 2021) are particularly prolific.…”

An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data

“…Concentrating on collaborator recommendations, Xi et al (2022) highlighted the similarity between the research interests of scholars and the topology of their collaboration networks, and developed a recommender system using word embedding and node embedding techniques for knowledge representation.…”