Real-world networks usually consist of a large number of interacting, multi-typed components which are usually referred as heterogeneous information networks (HIN). HIN that associated with various attributes on nodes is defined as attributed HIN (or AHIN). Clustering is a fundamental task for HIN and AHIN. However, most of the current existing methods focus on single type nodes and there is very limited existing work that groups objects of different types into the same cluster. This is largely due to the reasons that object similarities can either be attribute-based or link-based between same type of nodes and it is challenging to incorporate both in a unified framework. To bridge this gap, in this paper, we propose a framework, namely Cross Multi-Type Objects Clustering in Attributed Heterogeneous Information Network, or CMOC-AHIN, to integrate both the attribute information and multi-type node clustering in a principled way. We empirically show superior performances of CMOC-AHIN on three large scale challenging data sets and also summarize insights on the performances compared to other state-of-the-arts methodologies.

现实世界的网络通常由大量交互的，多类型的组件组成，这些组件通常称为异构信息网络（HIN）。与节点上各种属性相关联的HIN定义为属性HIN（或AHIN）。聚类是HIN和AHIN的基本任务。但是，当前大多数现有方法都集中在单一类型节点上，并且将不同类型的对象分组到同一集群中的现有工作非常有限。这主要是由于对象相似性可以在相同类型的节点之间基于属性或基于链接的原因，并且将两者都集成到统一框架中具有挑战性。为了弥合这一差距，本文提出了一个框架，即C ross M ulti-Type Objects Ç上光在甲ttributed ħ eterogeneous我载文信息Ñ etwork，或CMOC-AHIN，以便在一个原则性方式的属性信息和多类型节点聚类二者集成。我们从经验上展示了CMOC-AHIN在三个大型挑战性数据集上的优越性能，并总结了与其他最新技术方法相比的性能见解。