Abstract

Network embedding has gained more attentions in recent years. It has been shown that the learned low-dimensional node vector representations could advance a myriad of graph mining tasks such as node classification, community detection, and link prediction. A vast majority of the existing efforts are overwhelmingly devoted to single-layered networks or homogeneous networks with a single type of nodes and node interactions. However, in many real-world applications, a variety of networks could be abstracted and presented in a multi-layered fashion. Typical multi-layered networks include critical infrastructure systems, collaboration platforms, social recommender systems, to name a few. Despite the widespread use of multi-layered networks, it remains a daunting task to learn vector representations of different types of nodes due to the bewildering combination of both within-layer connections and cross-layer network dependencies. In this paper, we study a novel problem of multi-layered network embedding. In particular, we propose a principled framework-MANE to model both within-layer connections and cross-layer network dependencies simultaneously in a unified optimization framework for embedding representation learning. Experiments on real-world multi-layered networks corroborate the effectiveness of the proposed framework.

Original languageEnglish (US)
Pages684-692
Number of pages9
DOIs
StatePublished - Jan 1 2018
Event2018 SIAM International Conference on Data Mining, SDM 2018 - San Diego, United States
Duration: May 3 2018May 5 2018

Other

Other2018 SIAM International Conference on Data Mining, SDM 2018
CountryUnited States
CitySan Diego
Period5/3/185/5/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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    Li, J., Chen, C., Tong, H., & Liu, H. (2018). Multi-layered network embedding. 684-692. Paper presented at 2018 SIAM International Conference on Data Mining, SDM 2018, San Diego, United States. https://doi.org/10.1137/1.9781611975321.77