Advisor-Advisee Relationship Mining Based on Co-author Network

Advisor-advisee relationship among scholars is important in the academia circle. It contains abundant information about the academic inheritance, advisor recommendation and the forming of research communities, etc. The advisor-advisee relationship is always hiding behind the co-author network, however, there are some challenges when mining this kind of relationship. This relationship is always changing with time, the size of labeled data is limited and the authors’ name ambiguity, etc. Previous works are focused on various aspects, including the citation network, the publication network and the co-author network, etc. To our best knowledge, all of these works are focused on the whole network, and none of them considered the credit allocation of the authors in each paper. Therefore, the relationship mining results may be influenced greatly by some high degree nodes. In this paper, we proposed a new method to solve this problem with the scholar data in DBLP. The credit allocation of each author is calculated, and the co-author network of DBLP is cut into smaller networks based on the characteristic. Then, the advisor-advisee relationship among researchers is mined based on these smaller co-author network. The results show that, the accuracy of this model is about 62.5%, however, this is an unsupervised method, which could save the time of training model and will not be influenced by the uncompleted training data set.