Dongjie Zhu1, Yundong Sun1, Xiaofang Li2, Haiwen Du3, Rongning Qu2, Pingping Yu4, *, Xuefeng Piao1, Russell Higgs5, Ning Cao6
CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1343-1356, 2020, DOI:10.32604/cmc.2020.010008
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… More >
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