Network Embedding via Coupled Kernelized Multi-Dimensional Array Factorization

Linchuan Xu, Jiannong Cao, Xiaokai Wei, Philip S. Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Network embedding has been widely employed in networked data mining applications as it can learn low-dimensional and dense node representations from the high-dimensional and sparse network structure. While most existing network embedding methods only model the proximity between two nodes regardless of the order of the proximity, this paper proposes to explicitly model multi-node proximities which can be widely observed in practice, e.g., multiple researchers coauthor a paper, and multiple genes co-express a protein. Explicitly modeling multi-node proximities is important because some two-node interactions may not come into existence without a third node. By proving that LINE(1st), a recent network embedding method, is equivalent to kernelized matrix factorization, this paper proposes coupled kernelized multi-dimensional array factorization (Cetera) which jointly factorizes multiple multi-dimensional arrays by enforcing a consensus representation for each node. In this way, node representations can be more comprehensive and effective, which is demonstrated on three real-world networks through link prediction and multi-label classification.

Original languageEnglish
Article number8781900
Pages (from-to)2414-2425
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number12
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • kernelized array factorization
  • link prediction
  • multi-label classification
  • Network embedding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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