Feature-dependent graph convolutional autoencoders with adversarial training methods

Paper


Wu, Di, Hu, Ruiqi, Zheng, Yu, Jiang, Jing, Sharma, Nabin and Blumenstein, Michael. 2019. "Feature-dependent graph convolutional autoencoders with adversarial training methods." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8852314
Paper/Presentation Title

Feature-dependent graph convolutional autoencoders with adversarial training methods

Presentation TypePaper
AuthorsWu, Di, Hu, Ruiqi, Zheng, Yu, Jiang, Jing, Sharma, Nabin and Blumenstein, Michael
Journal or Proceedings TitleProceedings of 2019 International Joint Conference on Neural Networks (IJCNN)
Number of Pages8
Year2019
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN.2019.8852314
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/8852314
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding
Conference/Event2019 International Joint Conference on Neural Networks (IJCNN)
Event Details
2019 International Joint Conference on Neural Networks (IJCNN)
Parent
International Joint Conference on Neural Networks (IJCNN)
Delivery
In person
Event Date
14 to end of 19 Jul 2019
Event Location
Budapest, Hungary
Abstract

Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics. Most existing graph embedding approaches either embed the topological information and node features separately or learn one regularized embedding with both sources of information, however, they mostly overlook the interdependency between structural characteristics and node features when processing the graph data into the models. Moreover, existing methods only reconstruct the structural characteristics, which are unable to fully leverage the interaction between the topology and the features associated with its nodes during the encoding-decoding procedure. To address the problem, we propose a framework using autoencoder for graph embedding (GED) and its variational version (VEGD). The contribution of our work is two-fold: 1) the proposed frameworks exploit a feature-dependent graph matrix (FGM) to naturally merge the structural characteristics and node features according to their interdependency; and 2) the Graph Convolutional Network (GCN) decoder of the proposed framework reconstructs both structural characteristics and node features, which naturally possesses the interaction between these two sources of information while learning the embedding. We conducted the experiments on three real-world graph datasets such as Cora, Citeseer and PubMed to evaluate our framework and algorithms, and the results outperform baseline methods on both link prediction and graph clustering tasks.

KeywordsGraph Embedding; Graph Convolutional Neural Networks; Generative Adversarial Network
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
460502. Data mining and knowledge discovery
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field.

Byline AffiliationsUniversity of Technology Sydney
Northwest A&F University, China
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