Version 1
: Received: 13 June 2022 / Approved: 15 June 2022 / Online: 15 June 2022 (10:41:23 CEST)
How to cite:
Chen, X.; Hao, T.; Han, L.; Leng, M.; Chen, J.; Guo, J. Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint. Preprints2022, 2022060225. https://doi.org/10.20944/preprints202206.0225.v1
Chen, X.; Hao, T.; Han, L.; Leng, M.; Chen, J.; Guo, J. Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint. Preprints 2022, 2022060225. https://doi.org/10.20944/preprints202206.0225.v1
Chen, X.; Hao, T.; Han, L.; Leng, M.; Chen, J.; Guo, J. Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint. Preprints2022, 2022060225. https://doi.org/10.20944/preprints202206.0225.v1
APA Style
Chen, X., Hao, T., Han, L., Leng, M., Chen, J., & Guo, J. (2022). Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint. Preprints. https://doi.org/10.20944/preprints202206.0225.v1
Chicago/Turabian Style
Chen, X., Jing Chen and Jingfeng Guo. 2022 "Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint" Preprints. https://doi.org/10.20944/preprints202206.0225.v1
Abstract
In heterogeneous networks, the random walks based on meta-path requires prior knowledge and lacks flexibility. And the random walks based on non-meta-path only considers the number of node types, but does not consider the influence of schema and topology between node types in real networks. To solve the above problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint). Firstly, to realize the flexible walks, we design a Type strategy, which is the 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 the node selection strategy based on the adjacency relationship between nodes. The Type & Inner strategy can realize the random walks based on meta-path, 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 classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results shown that F1-Score and NMI of HNE-RWTIC outperform state-of-the-art approaches.
Keywords
heterogeneous network embedding; random walks; non-meta-path; type and node constraints
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
