For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS.
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This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19080102, the 13th Five-year Informatization Plan of Chinese Academy of Sciences, Grant No. XXH13504 and the Key Research Program of Frontier Sciences, CAS, Grant No. QYZDB-SSW-SMC004-02.
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Wang, Y., Yu, M., Shan, G. et al. VISPubComPAS: a comparative analytical system for visualization publication data. J Vis 22, 941–953 (2019). https://doi.org/10.1007/s12650-019-00585-2
- Scientific literature
- Topic extraction
- Visual analytics system
- Publication analysis
- Author analysis