Analysis of Academic Research Networks to Find Collaboration Partners
- 1.4k Downloads
Social network analysis has been used for decades to find behavioral patterns and relationships that exist between people in a network. Researchers have been collaborating for centuries with the aim of improving the quality of research, to broaden the scope of problems that they tackle, to speed up the output and to disseminate knowledge across authors. Sometimes it becomes difficult to find the right collaboration partner due to various reasons, the major one being the lack of data about individuals working in their chosen domain in geographically separated locations. In this paper, we explain how social network analysis can be used to help researchers in finding suitable collaboration partners with whom they have not worked in the past but can collaborate in the future. Further, we have considered two different analysis techniques – weighted and non-weighted graph and the results are compared based on the relevance of the outcomes.
KeywordsAcademic research network Social network analysis Collaborative research
It is observed that there has been an increase in the number of research topics that require interdisciplinary treatment, which makes it essential for departments or organizations belonging to different fields of knowledge to collaborate in the problem solving process [1, 2, 3]. To some extent, this rise can be attributed to the increasing specialization of individual academics and the broadening in scope of the problems that they tackle. Hence, it is possible to integrate scholarly communities and foster knowledge transfer between related fields through collaboration .
An academic research network is a network of researchers who are connected through relations like student-advisor, collaborations and citations. It can thus be used to analyze the collaboration relation among a group of authors. From the researcher’s point of view, people who have worked together previously are much more likely to succeed in future collaborations, as they understand each other’s areas of interest, approaches and methodologies . In this paper, we propose an automated tool, which can be used by researchers to visualize their collaboration network, and thereby finding the prospective collaboration partners using a graph theory based approach.
As a case study, academic research network of National Institute of Technology Karnataka (NITK) Surathkal, Mangalore, India is considered, and the extracted results have been analyzed using social network analysis techniques. The remainder of this paper is presented as follows: Sect. 2 deals with the related work in the field of academic research network analysis. Section 3 describes the proposed methodology. Section 4 describes the implementation details. Obtained results and discussion are presented in Sect. 5. Finally, the conclusion and future work are given in Sect. 6.
2 Related Work
Much of the previous research in this area has focused on co-authorship analysis to analyze the collaboration networks [6, 7]. The co-authorship networks exhibit characteristics similar to the much studied citation networks , but co-authorship implies a much stronger link than citation, which can occur without the authors knowing each other. Scientists who have authored a paper together are considered connected and a large number of such connections constitute the research network. Co-authorship analysis has been used to assess the collaboration among academic institutions in a certain geographical environment .
Scientific collaboration is accepted as a positive phenomenon and is found to have a significant influence on the performance of individual researchers and institutions, in terms of effectiveness, efficiency and productivity [9, 10]. The analysis of social networks has been widely used to understand the implications of the relationship patterns between researchers in various fields [11, 12]. Further, in  bibliometrics information for the period between 1999 and 2005 has been used.
3 Proposed Methodology for Development of Automated Tool
In step 3, the data stored is used to build the research network. From the data that is collected on papers authored by a researcher, his co-authors are found. The academic research network is represented in the form of an undirected Graph G= (V, E) where V denotes a finite set of nodes and E denotes a finite set of edges. Each node in V represents a researcher and each edge in E represents a co-authorship relation between a pair of researchers. In step 4, the network is analyzed using two different approaches, which are explained next.
3.1 Non-weighted Graph Approach
3.2 Weighted Graph Approach
The data on papers published by researchers from NITK during 2007–2012 are considered for co-authorship analysis. The researchers are provided with the option of viewing their collaboration network. The pseudo codes for both the analysis techniques are given next.
4.1 Pseudo Code
5 Results and Discussion
6 Conclusion and Future Work
The tool developed helps the researchers in finding collaboration partners successfully. The time taken to analyze the network is comparatively less in case of non-weighted graph as the processing time spent for calculating the weights of edges is saved. The results obtained are more accurate in case of weighted approach as more importance is given to the strength of past successful collaborations. The limitation of this approach is that determining the accuracy of results obtained is a subjective process. Further research can be extended in this area by grouping authors based on factors other than area of research i.e. geographical location, work experience or affiliating institute etc.
- 1.Toral, S.L., et al.: An exploratory social network analysis of academic research networks. In: 2011 Third International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE (2011)Google Scholar
- 2.Bessis, N., Bessis, N.: Grid Technology for Maximizing Collaborative Decision Management and Support: Advancing Effective Virtual Organizations. Information Science Reference (2009)Google Scholar
- 4.Nikhil, J., Ramage, D., Jurafsky, D.: A study of academic collaboration in computational linguistics with latent mixtures of authors. In: ACL HLT 2011, p. 124 (2011)Google Scholar
- 5.Cummings, J.N., Kiesler, S.: Who collaborates successfully?: prior experience reduces collaboration barriers in distributed interdisciplinary research. In: Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work. ACM (2008)Google Scholar
- 8.Garfield, E.: Citation Indexing: Its Theory and Application in Science, Technology, and Humanities, vol. 8. Wiley, New York (1979)Google Scholar
- 11.Monclar, R.S., et al.: Using social networks analysis for collaboration and team formation identification. In: 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE (2011)Google Scholar
- 12.Benckendorff, P.: Exploring the limits of tourism research collaboration: a social network analysis of co-authorship patterns in Australian and New Zealand tourism research. In: 20th Annual Council for Australian University Tourism and Hospitality Education Conference (CAUTHE 2010). University of Tasmania (2010)Google Scholar
- 13.Sayogo, D.S., et al.: Building the academic community of e-government research on cross-boundary information integration and sharing. In: 2012 45th Hawaii International Conference on System Science (HICSS). IEEE (2012)Google Scholar