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Graph-embedding Enhanced Attention Adversarial Autoencoder

Chen, Yurong (2020) Graph-embedding Enhanced Attention Adversarial Autoencoder. Master's Thesis, University of Pittsburgh. (Unpublished)

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When dealing with the graph data in real problems, only part of the nodes in the graph are labeled and the rest are not. A core problem is how to use this information to extend the labeling so that all nodes are assigned a label (or labels). Intuitively we can learn the patterns (or extract some representations) from those labeled nodes and then apply the patterns to determine the membership for those unknown nodes. A majority of previous related studies focus on extracting the local information representations and may suffer from lack of additional constraints which are necessary for improving the robustness of representation. In this work, we presented Graph- embedding enhanced attention Adversarial Autoencoder Networks (Great AAN), a new scalable generalized framework for graph-structured data representation learning and node classification. In our framework, we firstly introduce the attention layers and provide insights on the self-attention mechanism with multi-heads. Moreover, the shortest path length between nodes is incorporated into the self-attention mechanism to enhance the embedding of the node’s structural spatial information. Then a generative adversarial autoencoder is proposed to encode both global and local information and enhance the robustness of the embedded data distribution. Due to the scalability of our approach, it has efficient and various applications, including node classification, a recommendation system, and graph link prediction. We applied this Great AAN on multiple datasets (including PPI, Cora, Citeseer, Pubmed and Alipay) from social science and biomedical science. The experimental results demonstrated that our new framework significantly outperforms several popular methods.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Chen, Yurongyuc127@pitt.eduyuc127
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairZhan,
Committee MemberHu,
Committee MemberXiong,
Date: 27 September 2020
Date Type: Publication
Defense Date: 31 March 2020
Approval Date: 27 September 2020
Submission Date: 1 April 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 37
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Graph Embedding, Adversarial Autoencoder, Shortest Path Length Attention
Date Deposited: 27 Sep 2020 22:16
Last Modified: 27 Sep 2020 22:16


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