High-order proximity preserving information network hashing

Defu Lian, Kai Zheng*, Vincent W. Zheng, Yong Ge, Longbing Cao, Ivor W. Tsang, Xing Xie

*Corresponding author for this work

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

35 Citations (Scopus)


Information network embedding is an effective way for efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with increasing scale of networks. Hashing is a promising approach for accelerating these problems by orders of magnitude. However, no prior studies have been focused on seeking binary codes for information networks to preserve high-order proximity. Since matrix factorization (MF) unifies and outperforms several well-known embedding methods with high-order proximity preserved, we propose a MF-based Information Network Hashing (INH-MF) algorithm, to learn binary codes which can preserve high-order proximity. We also suggest Hamming subspace learning, which only updates partial binary codes each time, to scale up INH-MF. We finally evaluate INH-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results demonstrate that INH-MF can perform significantly better than competing learning to hash baselines in both tasks, and surprisingly outperforms network embedding methods, including DeepWalk, LINE and NetMF, in the task of node recommendation.

Original languageEnglish
Title of host publicationKDD '18
Subtitle of host publicationproceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450355520
Publication statusPublished - 2018
Externally publishedYes
Event24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom
Duration: 19 Aug 201823 Aug 2018


Conference24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/TerritoryUnited Kingdom


  • Information Network Hashing
  • Matrix Factorization
  • Hamming Subspace Learning


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