People Like Us: Mining Scholarly Data for Comparable Researchers

Abstract: We present the problem of finding comparable researchers for any given researcher. This problem has many motivations. Firstly, know thyself. The answers of where we stand among research community and who we are most alike may not be easily found by existing evaluations of ones' research mainly based on citation counts. Secondly, there are many situations where one needs to find comparable researchers e.g., for reviewing peers, constructing programming committees or compiling teams for grants. It is often done through an ad hoc and informal basis. Utilizing the large scale scholarly data accessible on the web, we address the problem of automatically finding comparable researchers. We propose a standard to quantify the quality of research output, via the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop a framework of comparison of researchers by sequence matching. Several variations of comparisons are considered including matching by quality of publication venue and research topics, and performing prefix matching. We evaluate our methods on a large corpus and demonstrate the effectiveness of our methods through examples. In the end, we identify several promising directions for further work.