@Article{cmc.2020.010008,
AUTHOR = {Dongjie Zhu, Yundong Sun, Xiaofang Li, Haiwen Du, Rongning Qu, Pingping Yu, Xuefeng Piao, Russell Higgs, Ning Cao},
TITLE = {MINE: A Method of Multi-Interaction Heterogeneous  Information Network Embedding},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1343--1356},
URL = {http://www.techscience.com/cmc/v63n3/38879},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010008}
}