Journal article
Hierarchy construction and classification of heterogeneous information networks based on RSDAEf
Data & knowledge engineering, v 127, 101790
May 2020
Abstract
Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.
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Details
- Title
- Hierarchy construction and classification of heterogeneous information networks based on RSDAEf
- Creators
- Jinli Zhang - Beijing University of TechnologyZongli Jiang - Beijing University of TechnologyYongping Du - Beijing University of TechnologyTong Li - Beijing University of TechnologyYida Wang - China United Network Communications GroupXiaohua Hu - Drexel University
- Publication Details
- Data & knowledge engineering, v 127, 101790
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000537325200002
- Scopus ID
- 2-s2.0-85078191885
- Other Identifier
- 991019167442704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems