Attention-Aware Encoder-Decoder Neural Networks for Heterogeneous Graphs of Things
Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are...
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| Vydáno v: | IEEE transactions on industrial informatics Ročník 17; číslo 4; s. 2890 - 2898 |
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| Jazyk: | angličtina |
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IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are hard to accurately express the complex semantics and attributes for those heterogeneous nodes and links in HGoT. To address this issue, we develop attention-aware encoder-decoder graph neural networks for HGoT, termed as HGAED. Specifically, we utilize the attention-based separate-and-merge method to improve the accuracy, and leverage the encoder-decoder architecture for implementation. In the heart of HGAED, the separate-and-merge processes can be encapsulated into encoding and decoding blocks. Then, blocks are stacked for constructing an encoder-decoder architecture to jointly and hierarchically fuse heterogeneous structures and contents of nodes. Extensive experiments on three real-world datasets demonstrate the superior performance of HGAED over state-of-the-art baselines. |
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| AbstractList | Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are hard to accurately express the complex semantics and attributes for those heterogeneous nodes and links in HGoT. To address this issue, we develop attention-aware encoder-decoder graph neural networks for HGoT, termed as HGAED. Specifically, we utilize the attention-based separate-and-merge method to improve the accuracy, and leverage the encoder-decoder architecture for implementation. In the heart of HGAED, the separate-and-merge processes can be encapsulated into encoding and decoding blocks. Then, blocks are stacked for constructing an encoder-decoder architecture to jointly and hierarchically fuse heterogeneous structures and contents of nodes. Extensive experiments on three real-world datasets demonstrate the superior performance of HGAED over state-of-the-art baselines. |
| Author | Li, Kenli Chen, Cen Zeng, Zeng Li, Yangfan Duan, Mingxing |
| Author_xml | – sequence: 1 givenname: Yangfan orcidid: 0000-0003-3640-5088 surname: Li fullname: Li, Yangfan email: yangfanli@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 2 givenname: Cen orcidid: 0000-0003-1389-0148 surname: Chen fullname: Chen, Cen email: chencen@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 3 givenname: Mingxing orcidid: 0000-0002-1049-6244 surname: Duan fullname: Duan, Mingxing email: duanmingxing@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China – sequence: 4 givenname: Zeng orcidid: 0000-0002-2405-0323 surname: Zeng fullname: Zeng, Zeng email: zeng_zeng@hotmail.com organization: Institute for Infocomm Research, Singapore – sequence: 5 givenname: Kenli orcidid: 0000-0002-2635-7716 surname: Li fullname: Li, Kenli email: lkl@hnu.edu.cn organization: College of Information Science and Engineering, Hunan University, Changsha, China |
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| SubjectTerms | Coders Computer architecture Decoding Encoding Fuses Graph neural network (GNN) Graph neural networks graph of things heterogeneous graph Informatics Internet of Things Internet of Things (IoT) Neural networks Nodes Semantics |
| Title | Attention-Aware Encoder-Decoder Neural Networks for Heterogeneous Graphs of Things |
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