2010 MiningAdvisorAdviseeRelationshi

• (Wang et al., 2010) ⇒ Chi Wang, Jiawei Han, Yuntao Jia, Jie Tang, Duo Zhang, Yintao Yu, and Jingyi Guo. (2010). “Mining Advisor-advisee Relationships from Research Publication Networks.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835833

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data Mining.
• General Terms: Algorithms, Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22Mining+advisor-advisee+relationships+from+research+publication+networks%22+2010
• http://portal.acm.org/citation.cfm?id=1835833&preflayout=flat#citedby

Quotes

Author Keywords

Relationship mining, Time-constrained probabilistic factor graph, Co-author network, Advisor-advisee prediction.

Abstract

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).

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MiningAdvisorAdviseeRelationshi	Yintao Yu Jie Tang Duo Zhang Chi Wang Yuntao Jia Jingyi Guo Jiawei Han			Mining Advisor-advisee Relationships from Research Publication Networks		KDD-2010 Proceedings	http://www.cs.uiuc.edu/~hanj/pdf/kdd10 cwang.pdf	10.1145/1835804.1835833		2010