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Title: Learning heterogeneous network embedding from text and links
Authors: Long, YF 
Xiang, R 
Lu, Q 
Xiong, D 
Huang, CR 
Bi, CL
Li, ML
Keywords: Network embedding
Heterogeneous network
Attention mechanism
Text processing
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2018, v. 6, p. 55850-55860 How to cite?
Journal: IEEE access 
Abstract: 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.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2873044
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
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The following publication Long, Y.F., Xiang, R., Lu, Q., Xiong, D., Huang, C.R., Bi, C.L., & Li, M.L. (2018). Learning heterogeneous network embedding from text and links. IEEE Access, 6, 55850-55860 is available at
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