High-order proximity preserving information network hashing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

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. The source code of INH-MF is available online1

Original languageEnglish (US)
Title of host publicationKDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1744-1753
Number of pages10
ISBN (Print)9781450355520
DOIs
StatePublished - Jul 19 2018
Externally publishedYes
Event24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom
Duration: Aug 19 2018Aug 23 2018

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/TerritoryUnited Kingdom
CityLondon
Period8/19/188/23/18

Keywords

  • Hamming Subspace Learning
  • Information Network Hashing
  • Matrix Factorization

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint

Dive into the research topics of 'High-order proximity preserving information network hashing'. Together they form a unique fingerprint.

Cite this