Abstract Purpose Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks. Design/methodology/approach We propose an academic collaborator recommendation model based on attributed network embedding (ACR-ANE), which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes. The non-local neighbors for scholars are defined to capture strong relationships among scholars. A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space. Findings 1. The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors. 2. It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously. Research limitations The designed method works for static networks, without taking account of the network dynamics. Practical implications The designed model is embedded in academic collaboration network structure and scholarly attributes, which can be used to help scholars recommend potential collaborators. Originality/value Experiments on two real-world scholarly datasets, Aminer and APS, show that our proposed method performs better than other baselines.

中文翻译：

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基于属性网络嵌入的学术合作者推荐

摘要目的基于现实世界的学术数据，本研究旨在利用网络嵌入技术挖掘学术关系，并研究所提出的嵌入模型在学术合作者推荐任务中的有效性。设计/方法/途径 我们提出了一种基于属性网络嵌入的学术合作者推荐模型（ACR-ANE），该模型可以增强学者嵌入，并充分利用网络的拓扑结构和多类型的学者属性。学者的非本地邻居被定义为捕捉学者之间的牢固关系。采用深度自动编码器将学术协作网络结构和学者属性编码为低维表示空间。研究结果 1. 提出的非本地邻居比一阶邻居更能描述现实世界中学者之间的关系。2.同时为学者推荐合作者时，要考虑学术合作网络的结构和学者属性。研究局限 所设计的方法适用于静态网络，没有考虑网络动态。实际意义所设计的模型嵌入了学术合作网络结构和学术属性，可用于帮助学者推荐潜在的合作者。对两个真实世界学术数据集 Aminer 和 APS 的原创性/价值实验表明，我们提出的方法比其他基线表现更好。