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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data via Contrastive Context Sharing

February 1, 2023

Authors

Siyue Xie

The Chinese University of Hongkong


Da Sun Handason Tam

The Chinese University of Hongkong


Wing Cheong Lau

The Chinese University of Hong Kong


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Data Mining and Knowledge Management

Downloads:

Download PDF

Abstract:

Graph Neural Networks (GNNs) have recently become a popular framework for semi-supervised learning on graph-structured data. However, typical GNN models heavily rely on labeled data in the learning process, while ignoring or paying little attention to the data that are unlabeled but available. To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. By sharing the contextual information among nodes estimated to be in the same class, different nodes can be correlated even if they are unlabeled and remote from each other in the graph. Models can therefore learn different combinations of contextual patterns, which improves the robustness of node representations. Additionally, motivated by recent advances in self-supervised learning, we augment the context sharing strategy by integrating with contrastive learning, which naturally correlates intra-class and inter-class data. Such operations utilize all available data for training and effectively improve a model's learning capacity. CoCoS can be easily extended to a wide range of GNN-based models with little computational overheads. Extensive experiments show that CoCoS considerably enhances typical GNN models, especially when labeled data are sparse in a graph, and achieves state-of-the-art or competitive results in real-world public datasets. The code of CoCoS is available online.

DOI:

10.1609/aaai.v36i4.20347


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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