Learning Heterogeneous Network Embedding from Text and Links

Yunfei Long, Rong Xiang, Qin Lu, Dan Xiong, Chu Ren Huang, Chenglin Bi, Minglei Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


Finding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10-9, indicating very significant improvement.

Original languageEnglish
Article number8478654
Pages (from-to)55850-55860
Number of pages11
JournalIEEE Access
Publication statusPublished - 1 Jan 2018


  • attention mechanism
  • heterogeneous network
  • Network embedding
  • text processing

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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