Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

Xiao Chen, Tong Hao, Li Han, Meng Leng, Jing Chen and Jingfeng Guo
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Xiao Chen: Research Center of Marine Sciences, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
Tong Hao: School of Public Health and Health Sciences, Tianjin University of Traditional Chinese Medicine, Tianjin 301600, China
Li Han: Research Center of Marine Sciences, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
Meng Leng: Research Center of Marine Sciences, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
Jing Chen: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Jingfeng Guo: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Mathematics, 2022, vol. 10, issue 15, 1-20

Abstract: In heterogeneous networks, random walks based on meta-paths require prior knowledge and lack flexibility. On the other hand, random walks based on non-meta-paths only consider the number of node types, but not the influence of schema and topology between node types in real networks. To solve these problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint). Firstly, to realize flexible walks, we design a Type strategy, which is a node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is a node selection strategy based on the adjacency relationship between nodes. The Type and Inner strategy can realize the random walks based on meta-paths, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes’ embedding based on the random walks and Skip-Gram. Finally, in the classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results show that HNE-RWTIC outperforms state-of-the-art approaches. In the classification task, in DBLP, AMiner-Top, and Yelp, the values of Micro-F1 and Macro-F1 of HNE-RWTIC are the highest: 2.25% and 2.43%, 0.85% and 0.99%, 3.77% and 5.02% higher than those of five other algorithms, respectively. In the clustering task, in DBLP, AMiner-Top, and Yelp networks, the NMI value is increased by 19.12%, 6.91%, and 0.04% at most, respectively.

Keywords: heterogeneous network embedding; random walks; non-meta-path; type and inner constraints (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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