Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

Abstract

While graph neural networks (GNNs) exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representations.

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 38th International Conference on Data Engineering, Kuala Lumpur, Malaysia, 2022 May 9-12

First Page

1571

Last Page

1572

ISBN

9781665408844

Identifier

10.1109/ICDE53745.2022.00162

Publisher

IEEE

City or Country

Kuala Lumpur, Malaysia

Additional URL

http://doi.org/10.1109/ICDE53745.2022.00162

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