Skill ranking of researchers via hypergraph

Abstract

Researchers use various skills in their works, such as writing, data analysis and experiments design. These research skills have greatly influenced the quality of their research outputs, as well as their scientific impact. Although many indicators have been proposed to quantify the impact of researchers, studies of evaluating their scientific research skills are very rare. In this paper, we analyze the factors affecting researchers’ skill ranking and propose a new model based on hypergraph theory to evaluate the scientific research skills. To validate our skill ranking model, we perform experiments on the PLOS ONE dataset and compare the rank of researchers’ skills with their papers’ citation counts and h-index. Finally, we analyze the patterns about how researchers’ skill ranking increased over time. Our studies also show the change patterns of researchers between different skills.

ST-AFN: a spatial-temporal attention based fusion network for lane-level traffic flow prediction

“…As one of the branches of machine learning, deep learning has promoted the development of various researches with its strong learning ability and excellent portability (LeYun, Bengio & Hinton, 2015;Kong et al, 2019). Wu et al (2018) trained the traffic data in three categories: hourly, daily, and weekly with a fully connected neural network in turn, and then fused those outputs to obtain the final result.…”