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- Title
MINE: A Method of Multi-Interaction Heterogeneous Information Network Embedding.
- Authors
Dongjie Zhu; Yundong Sun; Xiaofang Li; Haiwen Du; Rongning Qu; Pingping Yu; Xuefeng Piao; Higgs, Russell; Ning Cao
- 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 Multi- Interaction 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.
- Subjects
INFORMATION networks; EMBEDDINGS (Mathematics); SOCIAL networks
- Publication
Computers, Materials & Continua, 2020, Vol 63, Issue 3, p1343
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2020.010008