Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint <underline>G</underline>raph <underline>E</underline>mbedding and <underline>N</underline>onnegative <underline>M</underline>atrix <underline>F</underline>actorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.

中文翻译：

---

通过联合图嵌入和非负矩阵分解对异构信息网络进行聚类

许多源自自然和社会的复杂系统由多种类型的实体和异构交互组成，可以有效地建模为异构信息网络（HIN）。利用异构网络中对象和链接的丰富语义信息对异构网络进行结构分析具有重要意义。并且，聚类异构网络旨在将顶点分组为类，这有助于揭示底层系统的结构-功能关系。当前的算法独立地执行特征提取和聚类，这被批评为不能完全表征聚类的结构。在这项研究中，我们提出了一个联合 <underline>G</underline>raph <underline>E</underline>mbedding 和 <underline>N< 的学习模型GEJNMF)，其中通过利用网络的图嵌入和潜在结构来同时学习特征提取和聚类。我们制定了GEjNMF的目标函数，将异构网络聚类问题转化为一个有约束的优化问题，有效地解决了l 0-规范优化。GEjNMF的优点是在聚类的指导下选择特征，在提高性能的同时节省了算法的运行时间。在三个基准异构网络上的实验结果表明，与最先进的方法相比，GEjNMF 以最少的运行时间实现了最佳性能。此外，所提出的算法在来自各个领域的异构网络中具有鲁棒性。所提出的模型和方法为异构网络聚类提供了一种有效的替代方案。