KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2021-06-02
Print Publication Date2021-06
Embargo End Date2022-06-02
Permanent link to this recordhttp://hdl.handle.net/10754/669566
MetadataShow full item record
AbstractTraditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this problem, approaches that exploit meaningful alternative clusterings in data have been developed in recent years. Existing algorithms, including single view/multi-view multiple clustering methods, are designed for applications with i.i.d. data samples, and cannot handle the data samples with dependency presented in networks, especially in heterogeneous information networks (HIN). In this paper, we propose a framework (NetMCs) that can explore multiple clusterings in HIN. Specifically, NetMCs adopts a set of meta-path schemes with different semantics on HIN, and considers each meta-path scheme as a base clustering aspect. Guided by the meta-path schemes, NetMCs then introduces a variation of the skip-gram framework that can jointly optimize multiple clustering aspects, and simultaneously obtain the respective embedding representations and individual clusterings therein. To reduce redundancy between alternative clusterings, NetMCs utilizes an explicit regularization term to control the embedding diversity of the same nodes among different clustering aspects. Experiments on benchmark HIN datasets confirm the performance of NetMCs in generating multiple clusterings with high quality and diversity.
CitationWei, S., Yu, G., Wang, J., Domeniconi, C., & Zhang, X. (2021). Multiple clusterings of heterogeneous information networks. Machine Learning. doi:10.1007/s10994-021-06000-y
SponsorsThis work is partially supported by NSFC (Nos. 62031003, 62072380 and 61872300).
PublisherSpringer Science and Business Media LLC