Encoding robust representation for graph generation

Dongmian Zou, Gilad Lerman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were developed for graphs and graph signals. However, the mathematical properties of these methods are unclear, and training good generative models is difficult. This work proposes a graph generation model that uses a recent adaptation of Mallat's scattering transform to graphs. The proposed model is naturally composed of an encoder and a decoder. The encoder is a Gaussianized graph scattering transform, which is robust to signal and graph manipulation. The decoder is a simple fully connected network that is adapted to specific tasks, such as link prediction, signal generation on graphs and full graph and signal generation. The training of our proposed system is efficient since it is only applied to the decoder and the hardware requirements are moderate. Numerical results demonstrate state-of-the-art performance of the proposed system for both link prediction and graph and signal generation.

Original languageEnglish (US)
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
StatePublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2019 International Joint Conference on Neural Networks, IJCNN 2019

Bibliographical note

Funding Information:
This research has been supported by NSF award DMS-18-30418. 1We remark that the Euclidean and graph domains include scenarios whose underlying datasets have Euclidean and graph structures, respectively.

Publisher Copyright:
© 2019 IEEE.


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