Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding.

中文翻译：

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用于学术关系挖掘的归因协作网络嵌入

为科学数字图书馆的学者寻找有效和有效的定量表示一直是研究的重点。史无前例的学术数据集与当代机器学习和大数据技术相结合，使得从这个庞大且不断增长的学术数据池中对学者进行智能和自动分析成为可能。同时，网络嵌入技术的最新进展使我们能够减轻学术协作网络大规模和稀疏的挑战。在现实世界的学术社交网络中，学者伴随着各种属性或特征，例如共同作者和出版记录，这导致了属性协作网络。已经观察到，网络拓扑和学者属性在学术关系挖掘中都很重要。然而，以往的研究主要集中在网络拓扑上，而忽略了学者属性。此外，不同学者属性的影响尚不清楚。为了弥合这一差距，在这项工作中，我们提出了一个用于学术关系挖掘的属性协作网络嵌入 (ACNE) 的新框架。ACNE 基于提出的学者分析模型提取了四种类型的学者属性，包括人口统计、研究、影响力和社交能力。ACNE 可以同时考虑学者属性和网络拓扑来学习学者的低维表示。我们通过对两个真实世界的数据集进行合作者推荐来证明 ACNE 在学术关系挖掘中的有效性和潜力，并研究了每个学者属性对科学合作者推荐的贡献和重要性。我们的工作可以通过利用归因协作网络嵌入来阐明学术关系挖掘。