Accelerated attributed network embedding

Xiao Huang, Jundong Li, Xia Hu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

213 Citations (Scopus)


Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. It has been observed that network topological structure and node attributes are often strongly correlated with each other. Thus modeling and incorporating node attribute proximity into network embedding could be potentially helpful, though non-trivial, in learning better vector representations. Meanwhile, real-world networks often contain a large number of nodes and features, which put demands on the scalability of embedding algorithms. To bridge the gap, in this paper, we propose an accelerated attributed network embedding algorithm AANE, which enables the joint learning process to be done in a distributed manner by decomposing the complex modeling and optimization into many sub-problems. Experimental results on several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611974874
Publication statusPublished - 2017
Externally publishedYes
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: 27 Apr 201729 Apr 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017


Conference17th SIAM International Conference on Data Mining, SDM 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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