Universities play an important role in exploring new concepts and knowledge transfer. University research naturally forms heterogeneous graphs through all real-life academic communication activities. In recent years, there have been many large scholarly graph datasets containing web-scale nodes and edges. However, so far, for these graph data, characterizing research about university output is focusing on counting the volume or evaluating the excellence of research articles and providing a ranking. This paper proposes a novel University Profiling Framework (UPF) from the production and complexity point of view which is different from other straightforward solutions. The framework includes a novel Recurrent Deep Clustering Model (Recurrent-DC) for the learning of deep representations and clusters. In our model, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Stacked Autoencoder (SAE). Our key idea behind this model is that good representations for university clustering task-specific problem can be learned over multiple timesteps. Experimental results illustrate the stability and effectiveness of the proposed model comparing with the other deep clustering and classical clustering methods.

大学在探索新概念和知识转移方面发挥着重要作用。大学研究通过所有现实的学术交流活动自然形成异构图。近年来，已经有许多包含网络规模节点和边的大型学术图数据集。但是，到目前为止，对于这些图形数据，表征大学产出的研究的重点是统计数量或评估研究论文的卓越性并提供排名。本文从生产和复杂性的角度提出了一种新颖的大学分析框架（UPF），该框架不同于其他直接的解决方案。该框架包括一个新颖的递归深度聚类模型（Recurrent-DC），用于学习深度表示和聚类。在我们的模型中 聚类算法中的后续操作表示为循环过程中的步骤，这些步骤堆叠在由堆叠式自动编码器（SAE）输出的表示形式之上。该模型背后的关键思想是，可以在多个时间步骤中学习针对大学集群任务特定问题的良好表示形式。实验结果表明，与其他深度聚类和经典聚类方法相比，该模型的稳定性和有效性。