Abstract
Citations are references used by researchers to recognize the contributions of researchers in their articles. Citations can be used to discover hidden patterns in the research domain, and can also be used to perform various analyses in data mining. Citation analysis is a quantitative method to identify knowledge dissemination and influence papers in any research area. Citation analysis involves multiple techniques. One of the most commonly used techniques is Main Path Analysis (MPA). According to the specific use of MPA, it has evolved into various variants. Currently, MPA is carried out in different domains, but deep learning in the field of remote sensing has not yet been considered. In this paper, we have used three centrality attributes which are Degree, Betweenness and Closeness centrality to automatically identify important papers by applying clustering method based on machine learning (i.e., K-means). In addition, the main path is drawn from important papers and compared with existing manual methods. In order to conduct experiments, a data set from Web of Science (WOS) has been established, which contains 538 papers in the field of deep learning. Compared with existing works, our method provides the most relevant papers on the main path.