Finding Experts by Link Prediction in Co-authorship Networks
Finding Experts by Link Prediction in Co-authorship Networks
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发表时间：
2007-11
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通讯作者：
M. Pavlov;R. Ichise
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作者：
M. Pavlov;R. Ichise
研究合作总是受到鼓励，因为它们往往会产生良好的结果。然而，研究人员网络包含了大量不同学科的专家，个人研究人员很难决定哪些专家最适合自己的专业知识。因此，协作结果往往是不确定的，研究团队组织不善。我们提出了一种在网络中构建链接预测器的方法，其中节点可以代表研究人员和链接合作。在这种情况下，预测者可能会为未来的合作提供良好的建议。我们在研究人员合作网络上测试了我们的方法，并获得了令人振奋的准确性链接预测因子。这让我们相信，我们的方法在建立和维持强大的研究团队方面可能是有用的。它还可以帮助选择用于专家描述的词汇，因为链接预测器包含关于网络的哪些结构属性对于链接预测问题来说是重要的隐含信息。
Research collaborations are always encouraged, as they often yield good results. However, the researcher network contains massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best. As a result, collaboration outcomes are often uncertain and research teams are poorly organized. We propose a method for building link predictors in networks, where nodes can represent researchers and links - collaborations. In this case, predictors might offer good suggestions for future collaborations. We test our method on a researcher coauthorship network and obtain link predictors of encouraging accuracy. This leads us to believe our method could be useful in building and maintaining strong research teams. It could also help with choosing vocabulary for expert description, since link predictors contain implicit information about which structural attributes of the network are important with respect to the link prediction problem.