Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoenco...

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Bibliographic Details
Published in:Proceedings / IEEE International Conference on Computer Vision pp. 6518 - 6527
Main Authors: Park, Jiwoong, Lee, Minsik, Chang, Hyung Jin, Lee, Kyuewang, Choi, Jin Young
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Chebyshev approximation
Convolution
Decoding
Image reconstruction
Laplace equations
Smoothing methods
Task analysis
ISSN:2380-7504
Online Access:Get full text
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Summary:We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks. The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.
ISSN:2380-7504
DOI:10.1109/ICCV.2019.00662