Predicting Researchers' Future Activities Using Visualization System for Co-authorship Networks
DOI: 10.1109/WI-IAT.2011.96 Conference: Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on, Volume: 1
Source: IEEE Xplore
This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.
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ABSTRACT: This paper examines the effectiveness of a visu- alization system for getting insight into future research activ- ities from co-authorship networks. A co-authorship network is important information when doing a research survey. In particular, there are many requests on survey that relate with researchers' future activities, such as identification of growing researchers and supervisors. In previous paper we proposed a visualization system for co-authorship networks, which provides the function for identifying research areas and that for identifying temporal variation of both network structure and keyword distribution. This paper examines its effectiveness through field trials by test participants. The results are examined as the process of hypothesis verification, which shows that test participants could perform the task even though they had no background knowledge about InfoVis.
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ABSTRACT: The Brazilian Lattes Platform is an important academic/resume dataset that registers all of the academic activity of researchers associated with different major knowledge areas. Currently, the activity of over a million researchers has been registered in this dataset. The academic information collected in this dataset is used to evaluate, analyze, and document the scientific production of research groups. Information about the interactions between Brazilian researchers in the form of co-authorships, however, has not been analyzed. In this paper we identified and characterized Brazilian academic co-authorship networks of researchers registered in the Lattes Platform, using topological properties of graphs. For this purpose, we explored (i) strategies to develop a very large Lattes curricula dataset, (ii) an algorithm for identifying automatic co-authorships based on bibliographic information, and (iii) topological metrics to investigate interactions among researchers. The aim of our study was to characterize co-authorship networks to gain an in-depth understanding of the network structures and dynamics (social behavior) among researchers in all available Brazilian major knowledge areas. In this study, we evaluated information from a total of 1,131,912 researchers associated with the 8 major Brazilian knowledge areas: Agricultural Sciences; Biological Sciences; Exact and Earth Sciences; Humanities; Applied Social Sciences; Health Sciences; Engineering; and Linguistics, Letters and Arts.
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