Divide-and-conquer Test-time Adaptation with Retrieval-augmentation for Text-attributed Graph Foundation Model
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摘要: 文本属性图基础模型(TAG-FM)旨在通过在大规模图数据集上预训练, 实现稳健的跨域泛化能力. 尽管TAG-FM已在多项下游任务中展现出良好的性能, 但是通过深入分析发现, 其在泛化能力方面仍存在关键弱点: 域适应阈值效应, 即, 当预训练域与测试域之间的分布偏移超过某一临界阈值时, 模型性能将出现显著退化. 由于基础模型复杂度高且测试数据稀缺, 引入测试时自适应方法成为缓解该问题的可行方案. 然而现有测试时自适应方法在实例层和域层均存在局限, 严重制约着TAG-FM对分布外数据的适应性能. 针对此问题, 提出一种面向TAG-FM的“检索—分治”测试时自适应方法, 该方法通过检索增强的特征共形融合模块生成融合图结构和节点文本语义的统一特征, 并引入一种分而治之的测试时自适应策略, 通过渐进式自适应过程学习泛化性语义. 在7个基准文本属性图数据集和12个严重域偏移文本属性图数据集上的实验结果表明, 本文方法能够显著增强TAG-FM的跨域泛化能力.Abstract: Text-attributed graph foundation model (TAG-FM) is designed to attain robust cross-domain generalization capability through pretraining on large-scale graph datasets. Although TAG-FM has demonstrated promising performance on various downstream tasks, our in-depth analysis reveals a critical limitation in its generalization capability: The domain adaptation threshold effect, i.e., model performance undergoes catastrophic degradation when distributional shifts between pretraining and testing domains surpass critical thresholds. Due to the excessive foundation model complexity and scarcity of testing data, a feasible solution to mitigate this issue is to introduce test-time adaptation approaches for TAG-FM. Nevertheless, existing test-time adaptation approaches exhibit substantial limitations at both the instance and domain levels, severely constraining the ability of TAG-FM to adapt to out-of-distribution data. To address this issue, we innovatively propose the divide-and-conquer test-time adaptation with retrieval-augmentation for TAG-FM. Specifically, a retrieval-augmented feature conformal integration module is introduced to construct consolidated node features by integrating graph structures with node textual semantics. Furthermore, a divide-and-conquer test-time adaptation strategy is developed to progressively capture generalizable semantics through an iterative adaptation process. Empirically, we verify that the proposed method achieves the state-of-the-art performance on 7 benchmark text-attributed graph datasets and 12 constructed text-attributed graph datasets exhibiting severe domain shifts, which confirms that our method effectively enhances the cross-domain generalization capability of TAG-FM.1)
1 1本文将TAG-FM的预训练数据视为预训练域, 将Cora、CiteSeer等测试数据集视为测试域, 一个测试数据集可视为一个测试域. 值得注意的是, TAG-FM的预训练数据仅用于探索实验, 所提方法的实际实现并未使用任何TAG-FM预训练数据.2)2 2对任意节点$ v_n $, 均存在对应的统一特征$ z_n $, 故可反向利用$ z_n $检索节点$ v_n $. -
图 1 不同域偏移强度下TAG-FM性能及预训练域与测试域之间的KL散度
Fig. 1 TAG-FM performance and the KL divergence between the pretraining and testing domains under varying degrees of domain shifts
图 2 代表性实例对在不同编码策略下的语义相似度比较
Fig. 2 Comparison of semantic similarity for representative instance pairs across different encoding strategies
图 3 GraphCLIP在不同域偏移设置下的性能表现
Fig. 3 Performance of GraphCLIP under different domain shift settings
图 4 GraphCLIP+GTrans在不同域偏移下的性能表现
Fig. 4 Performance of GraphCLIP+GTrans under different domain shift settings
图 5 DORA的整体框架, 其中扰动$ \delta$随适应过程更新
Fig. 5 The overall framework of DORA, where the perturbation $ \delta$ is progressively updated during the adaptation process.
图 6 严重域偏移数据集上的节点分类任务准确率
Fig. 6 Accuracy for the node classification task on severe domain shift datasets
图 8 超参数$ \lambda$对模型性能的影响
Fig. 8 The impact of hyperparameter $ \lambda$ on model performance
图 9 Cora和CiteSeer数据集上GraphCLIP与DORA所得特征的t-SNE可视化
Fig. 9 t-SNE visualization of features produced by GraphCLIP and DORA on Cora and CiteSeer datasets
图 10 SDS-Cora系列严重域偏移数据集上GraphCLIP与DORA所得特征的t-SNE可视化
Fig. 10 t-SNE visualization of features produced by GraphCLIP and DORA on SDS-Cora series datasets with severe domain shifts
图 11 SDS-CiteSeer系列严重域偏移数据集上GraphCLIP与DORA所得特征的t-SNE可视化
Fig. 11 t-SNE visualization of features produced by GraphCLIP and DORA on SDS-CiteSeer series datasets with severe domain shifts
图 12 测试时自适应过程中相邻迭代步之间梯度的余弦相似度
Fig. 12 Cosine similarity of gradients between consecutive iterations during test-time adaptation.
图 13 测试时自适应过程中高确定性错误样本数量
Fig. 13 Number of misclassified samples with high certainty during test-time adaptation
图 14 GraphCLIP与DORA在Cora数据集中两个典型节点上的分类结果对比分析
Fig. 14 Comparative analysis of classification results of GraphCLIP and DORA on two representative nodes in the Cora dataset
表 1 文本属性图数据集统计
Table 1 Statistics of text-attributed graph datasets
数据集 节点数 边数 领域 类别数 Cora 2708 5429 学术 7 CiteSeer 3186 4277 学术 6 Ele-Photo 48362 500928 电子商务 12 Ele-Computers 87229 721081 电子商务 10 Books-History 41551 358574 电子商务 12 WikiCS 11701 215863 维基百科 10 Instagram 11339 144010 社交媒体 2 
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表 2 类别相关提示模板汇总
Table 2 Summary of the class-relevant prompt templates
数据集 提示模板 Cora this paper has a topic on {class} {class_desc} CiteSeer good paper of {class} {class_desc} Ele-Photo this product belongs to {class} {class_desc} Ele-Computers is {class} category {class_desc} Books-History this book belongs to {class} {class_desc} WikiCS it belongs to {class} research area {class_desc} Instagram {class} {class_desc}
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表 3 构建查询$ q_n$的提示模板汇总
Table 3 Summary of prompt templates used to construct the query $ q_n$
数据集 构建查询$q_n$的提示模板 Cora The textual description of the citation network centered on node ‘n{id}’ is shown above. Each node in the network represents a scholarly article, and each edge signifies a citation relationship between articles. The textual description of node ‘n{id}’ is: ‘{desc}’. Please summarize the core content of the article represented by node ‘n{id}’ using the information provided above in 200 words: CiteSeer The textual description of the citation network centered on node ‘n{id}’ is shown above. Each node in the network represents a scholarly article, and each edge signifies a citation relationship between articles. The textual description of node ‘n{id}’ is: ‘{desc}’. Please summarize the core content of the article represented by node ‘n{id}’ using the information provided above in 200 words: Ele-Photo The textual description of the Amazon Electronics graph centered on node ‘n{id}’ is shown above. Each node in the graph represents a electronic product, and each edge signifies frequent co-purchases or co-views. The user review with the most votes of node ‘n{id}’ is: ‘{desc}’. Please summarize the information provided above and re-describe the electronic product represented by node ‘n{id}’ based on the results of the summary in 200 words: Ele-Computers The textual description of the Amazon Electronics graph centered on node ‘n{id}’ is shown above. Each node in the graph represents a electronic product, and each edge signifies frequent co-purchases or co-views. The user review with the most votes of node ‘n{id}’ is: ‘{desc}’. Please summarize the information provided above and re-describe the electronic product represented by node ‘n{id}’ based on the results of the summary in 200 words: Books-
HistoryThe textual description of the Amazon-Books graph centered on node ‘n{id}’ is shown above. Each node in the graph represents a book focusing on items labeled as 'History', and each edge signifies frequent co-purchases or co-views between two books. The title and description of the book of node ‘n{id}’ is: ‘{desc}’. Please summarize the information provided above and re-describe the history book represented by node ‘n{id}’ based on the results of the summary in 200 words: WikiCS The textual description of the Wikipedia-based graph centered on node ‘n{id}’ is shown above. The textual description of node ‘n{id}’ is ‘{desc}’. Please summarize the information provided above and re-describe node ‘n{id}’ based on the results of the summary in 200 words: Instagram The textual description of the social network centered on node ‘n{id}’ is shown above. Each node in the graph signifys user, and each edge signifies following relationships. The textual description of node ‘n{id}’ is: ‘{desc}’. Please summarize the information provided above and re-describe the user represented by node ‘n{id}’ based on the results of the summary in 200 words:
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表 4 轻度域偏移数据集上的节点分类任务准确率(%)
Table 4 Accuracy for the node classification task on mild domain shift datasets(%)
方法 Cora CiteSeer WikiCS Instagram Ele-Photo Ele-Computers Books-History 平均值 BERT[58] 19.56 $\pm$ 0.98 33.26 $\pm$ 2.35 29.37 $\pm$ 0.00 57.02 $\pm$ 0.57 21.80 $\pm$ 0.14 13.88 $\pm$ 0.29 9.95 $\pm$ 0.42 26.41 SBERT[59] 54.35 $\pm$ 1.26 50.47 $\pm$ 0.90 48.16 $\pm$ 0.00 48.34 $\pm$ 1.23 35.96 $\pm$ 0.44 41.82 $\pm$ 0.22 30.45 $\pm$ 0.19 44.22 DeBERTa[60] 16.42 $\pm$ 1.26 16.42 $\pm$ 1.26 15.29 $\pm$ 0.00 39.81 $\pm$ 0.58 12.38 $\pm$ 0.26 10.62 $\pm$ 0.15 9.70 $\pm$ 0.26 17.23 E5[61] 44.65 $\pm$ 0.82 42.57 $\pm$ 0.54 31.49 $\pm$ 0.00 61.28 $\pm$ 0.97 35.14 $\pm$ 0.28 16.54 $\pm$ 0.14 12.92 $\pm$ 0.48 34.94 Qwen2-7B-Instruct[62] 61.44 $\pm$ 1.29 53.57 $\pm$ 0.86 58.72 $\pm$ 0.25 39.13 $\pm$ 0.78 45.55 $\pm$ 0.12 59.18 $\pm$ 0.20 23.79 $\pm$ 0.34 48.77 Qwen2-72B-Instruct[62] 62.18 $\pm$ 0.98 60.97 $\pm$ 0.87 60.91 $\pm$ 0.08 47.70 $\pm$ 0.31 52.41 $\pm$ 0.39 60.88 $\pm$ 0.30 53.56 $\pm$ 0.64 56.94 LLaMA3.1-8B-Instruct[11] 57.75 $\pm$ 1.21 53.54 $\pm$ 1.71 58.32 $\pm$ 0.21 39.37 $\pm$ 1.14 34.38 $\pm$ 0.25 46.98 $\pm$ 0.21 22.28 $\pm$ 0.18 44.66 LLaMA3.1-70B-Instruct[11] 65.72 $\pm$ 1.24 62.79 $\pm$ 1.24 62.82 $\pm$ 0.04 43.68 $\pm$ 0.52 51.26 $\pm$ 0.53 61.62 $\pm$ 0.42 53.33 $\pm$ 0.55 57.32 OFA[12] 37.25 $\pm$ 1.38 29.64 $\pm$ 0.19 45.52 $\pm$ 1.06 32.71 $\pm$ 0.16 33.03 $\pm$ 0.64 22.09 $\pm$ 0.39 16.87 $\pm$ 0.93 31.02 ZeroG[13] 62.32 $\pm$ 1.91 52.55 $\pm$ 1.23 54.93 $\pm$ 0.06 48.97 $\pm$ 0.78 45.12 $\pm$ 0.65 56.20 $\pm$ 0.35 40.74 $\pm$ 0.65 51.55 GraphGPT[14] 23.25 $\pm$ 1.45 18.04 $\pm$ 1.45 6.30 $\pm$ 0.26 45.12 $\pm$ 1.16 7.62 $\pm$ 0.22 29.71 $\pm$ 0.83 15.92 $\pm$ 0.14 20.85 LLaGA[15] 21.44 $\pm$ 0.65 16.07 $\pm$ 1.15 2.65 $\pm$ 0.72 41.12 $\pm$ 0.94 6.50 $\pm$ 0.53 23.10 $\pm$ 0.33 11.17 $\pm$ 0.58 17.44 DGI[63] 24.03 $\pm$ 1.40 18.71 $\pm$ 1.22 18.86 $\pm$ 0.25 61.42 $\pm$ 1.12 13.96 $\pm$ 0.17 27.12 $\pm$ 0.03 15.77 $\pm$ 0.02 25.70 GRACE[64] 13.69 $\pm$ 1.27 22.88 $\pm$ 1.49 16.07 $\pm$ 0.32 62.23 $\pm$ 0.93 10.16 $\pm$ 0.13 10.94 $\pm$ 0.12 32.39 $\pm$ 0.11 24.05 BGRL[65] 31.99 $\pm$ 1.06 26.50 $\pm$ 1.22 18.35 $\pm$ 0.22 61.45 $\pm$ 0.82 5.21 $\pm$ 0.22 24.12 $\pm$ 0.22 16.28 $\pm$ 0.35 26.27 GraphMAE[66] 23.25 $\pm$ 1.07 20.75 $\pm$ 0.88 12.14 $\pm$ 0.20 62.39 $\pm$ 0.84 12.53 $\pm$ 0.08 8.36 $\pm$ 0.06 21.76 $\pm$ 0.17 23.03 G2P2[67] 41.51 $\pm$ 0.78 51.02 $\pm$ 0.62 31.92 $\pm$ 0.15 52.87 $\pm$ 0.78 22.21 $\pm$ 0.12 32.52 $\pm$ 0.13 26.18 $\pm$ 0.25 36.89 GraphCLIP[19] 67.31 $\pm$ 1.76 63.13 $\pm$ 1.13 70.19 $\pm$ 0.10 64.05 $\pm$ 0.34 53.40 $\pm$ 0.64 62.04 $\pm$ 0.21 53.88 $\pm$ 0.35 62.00 DORA 70.29 $\pm$ 0.54 67.82 $\pm$ 0.52 72.42 $\pm$ 0.67 64.83 $\pm$ 0.25 54.93 $\pm$ 0.42 63.25 $\pm$ 0.50 54.69 $\pm$ 0.43 64.03
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表 5 不同测试时自适应方法在轻度域偏移数据集上的节点分类任务准确率(%)
Table 5 Accuracy for the node classification task on mild domain shift datasets with different TTA methods(%)
方法 Cora CiteSeer WikiCS Instagram Ele-Photo Ele-Computers Books-History 平均值 Tent[21] 64.27 $\pm$ 1.35 63.06 $\pm$ 2.49 57.27 $\pm$ 0.83 50.56 $\pm$ 0.39 44.90 $\pm$ 0.14 49.35 $\pm$ 0.20 24.99 $\pm$ 0.48 50.63 GTrans[24] 69.37 $\pm$ 0.60 66.98 $\pm$ 1.12 71.89 $\pm$ 0.77 64.02 $\pm$ 0.19 54.83 $\pm$ 0.29 61.16 $\pm$ 0.37 50.92 $\pm$ 0.28 62.74 Matcha[22] 69.49 $\pm$ 1.13 66.78 $\pm$ 1.39 72.82 $\pm$ 0.89 64.02 $\pm$ 0.50 51.34 $\pm$ 0.74 56.06 $\pm$ 0.30 45.34 $\pm$ 1.35 60.84 DORA 70.29 $\pm$ 0.54 67.82 $\pm$ 0.52 72.42 $\pm$ 0.67 64.83 $\pm$ 0.25 54.93 $\pm$ 0.42 63.25 $\pm$ 0.50 54.69 $\pm$ 0.43 64.03
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表 6 消融实验结果(%)
Table 6 The results of the ablation experiment (%)
RFCI D&C-TTA Cora WikiCS Instagram RAS DFCI $\times$ $\times$ $\times$ 67.31 $\pm$ 1.76 70.19 $\pm$ 0.10 64.05 $\pm$ 0.34 $\times$ $ \checkmark$ $ \checkmark$ 69.13 $\pm$ 1.23 71.39 $\pm$ 0.74 64.21 $\pm$ 0.33 $ \checkmark$ $\times$ $ \checkmark$ 70.23 $\pm$ 0.57 72.16 $\pm$ 0.68 64.60 $\pm$ 0.31 $ \checkmark$ $ \checkmark$ $\times$ 70.05 $\pm$ 0.46 71.41 $\pm$ 1.35 64.80 $\pm$ 0.28 $ \checkmark$ $ \checkmark$ $ \checkmark$ 70.29 $\pm$ 0.54 72.42 $\pm$ 0.67 64.83 $\pm$ 0.25
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