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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Published in:	IEEE transactions on industrial informatics Vol. 17; no. 4; pp. 2890 - 2898
Main Authors:	Li, Yangfan, Chen, Cen, Duan, Mingxing, Zeng, Zeng, Li, Kenli
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
ISSN:	1551-3203, 1941-0050
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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.
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
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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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