Journal Article10.48550/arXiv.2306.04979
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Nan Yin,Libin Shen,Mengzhu Wang,Long Lan,Zeyu Ma,Cheng Chen,Xian-Sheng Hua,Xiao Luo +7 more
TL;DR: Coupled Contrastive Graph Representation Learning (CoCoCo) as discussed by the authors extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning.
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Abstract: Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network branch and a hierarchical graph kernel network branch, which explore graph topology in implicit and explicit manners. Besides, we incorporate coupled branches into a holistic multi-view contrastive learning framework, which not only incorporates graph representations learned from complementary views for enhanced understanding, but also encourages the similarity between cross-domain example pairs with the same semantics for domain alignment. Extensive experiments on popular datasets show that our CoCo outperforms these competing baselines in different settings generally.
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Citations
SA-GDA: Spectral Augmentation for Graph Domain Adaptation
Jinhui Pang,Zixuan Wang,Jiliang Tang,Mingyan Xiao,Nan Yin +4 more
- 26 Oct 2023
TL;DR: The Spectral Augmentation for Graph Domain Adaptation (SA-GDA) for graph node classification is presented, a dual graph convolutional network is developed to jointly exploits local and global consistency for feature aggregation, and a domain classifier with an adversarial learning submodule is utilized to facilitate knowledge transfer between different domain graphs.
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GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Junyu Luo,Yiyang Gu,Xiao Luo,Wei Ju,Zhiping Xiao,Yusheng Zhao,Jingyang Yuan,Ming Zhang +7 more
TL;DR: GALA is a novel method for source-free graph domain adaptation that employs a graph diffusion model to reconstruct source-style graphs from target data. It introduces perturbations to target graphs and combines confident and unconfident graphs to enhance generalization capabilities.
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A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning
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