Binarized attributed network embedding

Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

17 Citations (Scopus)


Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Data Mining (ICDM 2018)
EditorsDacheng Tao, Bhavani Thuraisingham
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538691595, 9781538691588
ISBN (Print)9781538691601
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Data Mining 2018 - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018


ConferenceIEEE International Conference on Data Mining 2018
Abbreviated titleICDM 2018
Internet address


  • Attributed network embedding
  • Learning to hash
  • Weisfeiler-Lehman graph kernels

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