Mining advisor-advisee relationships from research publication networks
Mining advisor-advisee relationships from research publication networks
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DOI：
10.1145/1835804.1835833
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Chi Wang;Jiawei Han;Yuntao Jia;Jie Tang;Duo Zhang;Yintao Yu;Jingyi Guo
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文献类型：
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作者：
Chi Wang;Jiawei Han;Yuntao Jia;Jie Tang;Duo Zhang;Yintao Yu;Jingyi Guo
信息网络包含有关人或实体之间关系的丰富知识。不幸的是，这种知识通常隐藏在没有明确分类的网络中。例如，在研究出版物网络中，研究人员之间的顾问 - 顾问关系隐藏在合着者网络中。这些关系的发现可以使许多有趣的应用程序受益，例如专家发现和研究社区分析。在本文中，我们以计算机科学书目网络为例，以分析作者的作用并发现可能的顾问 - 阿迪维西人的关系。特别是，我们提出了一个时间约束的概率因素图模型（TPFG），该模型将研究出版网络作为输入，并使用共同的可能性目标函数对顾问-Advisee关系挖掘问题进行建模。我们进一步设计了一种有效的学习算法来优化目标函数。基于此，我们的模型建议并为每个作者提供可能的顾问。实验结果表明，所提出的方法有效地推断了顾问 - 顾问 - 依次关系并达到最先进的准确性（80-90％）。我们还将发现的顾问 - 阿德维西人的关系应用于Bole搜索，这是一项特定的专家发现任务和经验研究表明，搜索性能可以有效提高（NDCG@5）。
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).