Discrete network embedding

Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew Soon Ong, Quan Sen Sun

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

32 Citations (Scopus)


Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
ISBN (Electronic)9780999241127
Publication statusPublished - 2018
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018
Conference number: 27th
https://www.ijcai.org/proceedings/2018/ (Proceedings)


ConferenceInternational Joint Conference on Artificial Intelligence 2018
Abbreviated titleIJCAI 2018
Internet address


  • Machine Learning
  • Data Mining
  • Relational Learning
  • Machine Learning Applications
  • Networks

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