【视频摘要】《中国科学：信息科学》呈献 | CareerMap: Visualizing Career Trajectory

文章视频摘要

正文

This study introduces a system named CareerMap that visualizes a scholar’s career trajectory. As an online demonstration of CareerMap, we have shown the visualization result by CareerMap for the AMiner 2016 most influential scholars in machine learning (ML)1). Each trajectory path on the map represents the movement of a scholar between different places (affiliations). The heatmap reveals the geographic distribution of the most influential ML scholars; a larger hotspot means a larger immigration of scholars into an affiliation. The right sidebar is a list of all scholars. When the user selects (clicks on) a scholar, the trajectory path of that scholar is highlighted in the map. The bottom bar shows the timeline. When the user selects a specific year, a textbox at the bottom displays the most important work (paper) published in that year by a scholar in the right-hand list. This example provides the track records of over half of the most influential ML scholars at the east and west coasts of the USA, and in west Europe. By zooming in, the user can also check the city-level results or obtain finer details. CareerMap can benefit many applications. For example, if the movements of all experts in artifi- cial intelligence (AI) worldwide were displayed on a visual map, government strategy departments could better understand the talent distribution and accordingly design wise AI strategies. Similarly, CareerMap can assist the design of smart recruiting plans by human resource (HR) departments of companies seeking talented employees. Individual users such as students can use the map to locate the best advisors for their Ph.D. studies. A scholar’s trajectory information is extracted from scientific publication data in AMiner, which has collected 130000000 researcher profiles and more than 200000000 papers from multiple publication databases. Since operations began in 2006, the system has attracted more than 8000000 independent IP accesses from over 200 countries/regions. Our extracted trajectory information is represented as a five-tuple of hname, affi, year, longitude, latitudei, where name and affi represent the scholar’s name and affiliation (extracted from the publication papers published by the scholar), respectively, year is the publication year of the extracted paper, and longitude and latitude are the geographic location information inferred from the extracted affiliation using Google map API. The main problem is extracting all the accurate affiliation information of all authors.

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SCIENCE CHINA Information Sciences (Sci China Inf Sci), cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results of both basic and applied research in all areas of information sciences, including computer science and technology; control science and engineering; information and communication engineering; microelectronics and solid state electronics, etc. SCIENCE CHINA Information Sciences is published monthly in both print and electronic forms. It is indexed by Science Citation Index Expanded (SCIE), Engineering Index (EI), Journal Citation Reports/Science Edition (JCR), etc.
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