Open AccessAdvisor-advisee relationship identification based on maximum entropy model
Author(s) -
Yongjun Li,
Zun Liu,
Yu Hui
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.168902
Subject(s) - computer science , identification (biology) , entropy (arrow of time) , social network (sociolinguistics) , feature selection , social network analysis , data mining , data science , artificial intelligence , world wide web , social media , physics , biology , botany , quantum mechanics
Research collaboration network has become an essential part in our academic activities. We can keep or develop collaboration relationships with other researchers or share research results with them within the research collaboration network. It is well generally accepted that different relationships have essentially different influences on the collaboration of researchers. Such a scenario also happens in our daily life. The advisor-advisee relationship plays an important role in the research collaboration network, so identification of advisor-advisee relationship can benefit the collaboration of researchers. In this paper, we aim to conduct a systematic investigation of the problem of indentifying the social relationship types from publication networks, and try to propose an easily computed and effective solution to this problem. Based on the common knowledge that graduate student always co-authors his papers with his advisor and not vice versa, our study starts with an analysis on publication network, and retrieves these features that can represent the advisor-advisee relationship. According to these features, an advisor-advisee relationship identification algorithm based on maximum entropy model with feature selection is proposed in this paper. We employ the DBLP dataset to test the proposed algorithm. The results show that 1) the mean of deviation of estimated end year to graduation year is 1.39; 2) the accuracy of advisor-advisee relationship identification results is more than 95%, and it is better than those of other algorithms obviously. Finally, the proposed algorithm can be extended to the relationship identification in online social network.