Open Access

ARTICLE

MINE: A Method of Multi-Interaction Heterogeneous Information Network Embedding

Dongjie Zhu¹, Yundong Sun¹, Xiaofang Li², Haiwen Du³, Rongning Qu², Pingping Yu^{4, *}, Xuefeng Piao¹, Russell Higgs⁵, Ning Cao⁶

1 School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China.
2 Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China.
3 School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China.
4 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
5 School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland.
6 School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China

* Corresponding Author: Pingping Yu. Email: .

Computers, Materials & Continua 2020, 63(3), 1343-1356. https://doi.org/10.32604/cmc.2020.010008

Received 04 February 2020; Accepted 20 February 2020; Issue published 30 April 2020

Abstract

Interactivity is the most significant feature of network data, especially in social networks. Existing network embedding methods have achieved remarkable results in learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly, we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships. Finally, applying a multitasking model makes the learned vector contain richer semantic relationships. A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

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Citations