While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

中文翻译：

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Scholar2vec：用于终身合作者预测的学者向量表示

虽然科学合作对学者来说至关重要，但一些合作者可能比其他人更重要，例如终身合作者。研究表明，终身合作者对学者的学业成绩影响更大。然而，很少有研究调查预测学术网络中的这种特殊关系。为此，我们提出了 Scholar2vec，这是一种用于表示学者资料的新型神经网络嵌入。首先，我们的方法从文本信息中创建学者的研究兴趣向量，例如人口统计、研究和影响力。在将研究兴趣与协作网络联系起来后，可以通过图学习获得学者的向量表示。同时，由于学者被各种属性所占据，我们建议将四种类型的学者属性用于学习学者向量。最后，利用基于 Scholar2vec 的早期相似度序列，通过机器学习方法预测生命周期的合作者。对两个真实世界数据集的广泛实验表明，Scholar2vec 在生命周期合作者预测方面优于最先进的方法。我们的工作提出了一种通过向量表示来衡量两位学者之间相似性的新方法，它解决了网络嵌入和学术关系挖掘之间的知识。对两个真实世界数据集的广泛实验表明，Scholar2vec 在生命周期合作者预测方面优于最先进的方法。我们的工作提出了一种通过向量表示来衡量两位学者之间相似性的新方法，它解决了网络嵌入和学术关系挖掘之间的知识。对两个真实世界数据集的广泛实验表明，Scholar2vec 在生命周期合作者预测方面优于最先进的方法。我们的工作提出了一种通过向量表示来衡量两位学者之间相似性的新方法，它解决了网络嵌入和学术关系挖掘之间的知识。