research-article

A Fully Test-time Training Framework for Semi-supervised Node Classification on Out-of-Distribution Graphs

Authors:

En ZhuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 7

Article No.: 172, Pages 1 - 19

https://doi.org/10.1145/3649507

Published: 19 June 2024 Publication History

Abstract

Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT proposes a fully test-time training framework for GNNs to enhance the model’s generalization capabilities for node classification tasks. Specifically, our proposed HomoTTT designs a homophily-based and parameter-free graph contrastive learning task with adaptive augmentation to guide the model’s adaptation during the test-time training, allowing the model to adapt for specific target data. In the inference stage, HomoTTT proposes to integrate the original GNN model and the adapted model after TTT using a homophily-based model selection method, which prevents potential performance degradation caused by unconstrained model adaptation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of our proposed framework. Additionally, the exploratory study further validates the rationality of the homophily-based graph contrastive learning task with adaptive augmentation and the homophily-based model selection designed in HomoTTT.

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Cited By

Duong HPham HTruong TFournier-Viger P(2024)Efficient algorithms to mine concise representations of frequent high utility occupancy patternsApplied Intelligence10.1007/s10489-024-05296-254:5(4012-4042)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05296-2

Index Terms

A Fully Test-time Training Framework for Semi-supervised Node Classification on Out-of-Distribution Graphs
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 7

August 2024

505 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3613689

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 19 June 2024

Online AM: 26 February 2024

Accepted: 21 January 2024

Revised: 14 January 2024

Received: 15 September 2023

Published in TKDD Volume 18, Issue 7

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Out-of-distribution; contrastive learning; graph neural network;

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National Key R&D Program of China

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Cited By

Duong HPham HTruong TFournier-Viger P(2024)Efficient algorithms to mine concise representations of frequent high utility occupancy patternsApplied Intelligence10.1007/s10489-024-05296-254:5(4012-4042)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05296-2

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