"Graph Neural Networks with Adaptive Residual" by Xiaorui Liu, Jiayuan Ding et al.
 

Graph Neural Networks with Adaptive Residual

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks. In this work, we discover an interesting phenomenon that although residual connections in the message passing of GNNs help improve the performance, they immensely amplify GNNs’ vulnerability against abnormal node features. This is undesirable because in real-world applications, node features in graphs could often be abnormal such as being naturally noisy or adversarially manipulated. We analyze possible reasons to understand this phenomenon and aim to design GNNs with stronger resilience to abnormal features. Our understandings motivate us to propose and derive a simple, efficient, interpretable, and adaptive message passing scheme, leading to a novel GNN with Adaptive residual, AirGNN1. Extensive experiments under various abnormal feature scenarios demonstrate the effectiveness of the proposed algorithm.

Identifier

85125027645 (Scopus)

ISBN

[9781713845393]

Publication Title

Advances in Neural Information Processing Systems

ISSN

10495258

First Page

9720

Last Page

9733

Volume

12

Grant

CNS1815636

Fund Ref

National Science Foundation

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