Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2022

Abstract

Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semisupervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. Given the lack of supervision on OOD nodes, we introduce a latent variable to indicate in-distribution or OOD nodes in a variational inference framework, and further propose a novel algorithm named as Learning to Mix Neighbors (LMN) which learns to dampen the influence of OOD nodes through the messaging-passing in typical graph neural networks. Extensive experiments on various datasets show that the proposed method outperforms state-of-the-art baselines in terms of both node classification and OOD detection.

Keywords

Data Mining: Mining Graphs, Machine Learning: Semi-Supervised Learning

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, 2022 July 23-29

First Page

2087

Last Page

2093

Identifier

10.24963/ijcai.2022/290

Publisher

IJCAI

City or Country

Vienna, Austria

Additional URL

http://doi.org/10.24963/ijcai.2022/290

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