document: Mining advisor-advisee relationships from research publication networks Information network contains abundant knowledge about relationships among people or entities . Unfortunately , such kind of knowledge is often hidden in a network where different kinds of relationships are not explicitly categorized . For example , in a research publication network , the advisor-advisee relationships among researchers are hidden in the coauthor network . Discovery of those relationships can benefit many interesting applications such as expert finding and research community analysis . In this paper , we take a computer science bibliographic network as an example , to analyze the roles of authors and to discover the likely advisor-advisee relationships . In particular , we propose a time-constrained probabilistic factor graph model ( TPFG ) , which takes a research publication network as input and models the advisor-advisee relationship mining problem using a jointly likelihood objective function . We further design an efficient learning algorithm to optimize the objective function . Based on that our model suggests and ranks probable advisors for every author . Experimental results show that the proposed approach infer advisor-advisee relationships efficiently and achieves a state-of-the-art accuracy ( 80-90 % ) . We also apply the discovered advisor-advisee relationships to bole search , a specific expert finding task and empirical study shows that the search performance can be effectively improved ( +4.09 % by NDCG@5 ) .
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