Recommending intra-institutional scientific collaboration through coauthorship network visualization

Parada, Gustavo A.; Ceballos, Héctor G.; Cantú, Francisco; Rodríguez-Aceves, Lucia

doi:10.1145/2508497.2508499

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Among the limited such studies, the focus has been mainly on intra-institutional collaboration. One such study used a social-network approach to compute the collaboration index between two bodies, which are intra-institutional (Parada et al, 2013). Another small study included a curated list of six universities, whose data-international academic and research orientations-were observed and a system was devised to provide recommendations for collaboration among them (Hernandez-Gress, Ceballos, & Galeano, 2018).…”

Section: Related Workmentioning

confidence: 99%

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Lathabai

Nandy

Singh

2022

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Lathabai

Nandy

Singh

2022

Expert Systems with Applications

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“…However, evidence from the literature suggests that existing collaborations affect future collaborations [37]. Additionally, best practices for scientific collaboration state that closing triangles (i.e., collaborating with one's collaborators' collaborators) is important [38].…”

Section: Old Usersmentioning

confidence: 99%

Using an Exponential Random Graph Model to Recommend Academic Collaborators

2019

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Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users.

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“…CANs have recently been used to study different aspects of research collaboration by extracting various types of information. Some key examples for these examined aspects are: relating authorship patterns with innovation (He et al, 2013;Lungeanu and Contractor, 2015), summarizing best practices in scientific collaboration, and making recommendations for institutional cooperation (Parada et al, 2013), recommendations for cooperation within specific academic fields (Holzinger et al, 2013;Velden and Lagoze, 2009;Xu et al, 2014;Cheong and Corbitt, 2009), or for interdisciplinary collaboration (Valdez et al, 2014). Some scholars have further enhanced these type of studies, and tried to use CAN to infer about researchers' research impact (Ortega, 2014), to determine their future publication likelihood (Kurosawa and Takama, 2012), for generating article recommendation systems (Taraghi et al, 2013), and to define the collaboration potential between authors (Giuliani et al, 2010).…”

Section: Relevant Literaturesmentioning

confidence: 99%

A typology of collaborative research networks

Hayat

2017

OIR

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Purpose Many studies have investigated how the structure of the collaborative networks of researchers influences the nature of their work, and its outcome. Co-authorship networks (CANs) have been widely looked at as proxies that can help bring understanding to the structure of research collaborative ties. The purpose of this paper is to provide a framework for describing what influences the formation of different research collaboration patterns. Design/methodology/approach The authors use social network analysis (SNA) to analyze the co-authorship ego networks of the ten most central authors in 24 years of papers (703 papers and 1,118 authors) published in the Proceedings of CASCON, a computer science conference. In order to understand what lead to the formation of the different CANs the authors examined, the authors conducted semi-structured interviews with these authors. Findings Based on this examination, the authors propose a typology that differentiates three styles of co-authorship: matchmaking, brokerage, and teamwork. The authors also provide quantitative SNA-based measures that can help place researchers’ CAN into one of these proposed categories. Given that many different network measures can describe the collaborative network structure of researchers, the authors believe it is important to identify specific network structures that would be meaningful when studying research collaboration. The proposed typology can offer guidance in choosing the appropriate measures for studying research collaboration. Originality/value The results presented in this paper highlight the value of combining SNA analysis with interviews when studying CAN. Moreover, the results show how co-authorship styles can be used to understand the mechanisms leading to the formation of collaborative ties among researchers. The authors discuss several potential implications of these findings for the study of research collaborations.

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Recommending intra-institutional scientific collaboration through coauthorship network visualization

Cited by 7 publications

References 22 publications

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis

Using an Exponential Random Graph Model to Recommend Academic Collaborators

A typology of collaborative research networks

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