ZeroG：探究图中的跨数据集零样本迁移能力
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
AI总结：
针对图学习跨数据集零样本迁移的特征不对齐等挑战，研究提出ZeroG框架，结合语言模型编码、提示式子图采样与轻量微调，实现了显著的跨数据集零样本迁移效果，助力图基础模型发展。
摘要：
随着大语言模型等基础模型的发展，零样本迁移学习的重要性日益凸显。GPT-4等NLP模型的生成能力、CLIP等CV模型的基于检索的方法都体现了这一点，二者均有效弥合了已见数据与未见数据之间的差距。在图学习领域，新图不断涌现，人工标注存在挑战，这也放大了零样本迁移学习的必要性，推动研究者探索无需针对特定数据集和特定标签进行微调，就能在多样化图数据上泛化的方法。本研究将这类范式拓展至图中的零样本迁移，推出专门用于实现跨数据集泛化的新框架ZeroG。针对特征不对齐、标签空间不匹配、负迁移等固有挑战，我们利用语言模型对节点属性和类别语义进行编码，确保不同数据集的特征维度一致。我们还提出了基于提示的子图采样模块，分别通过提示节点和邻域聚合来丰富提取出的子图的语义信息与结构信息。我们进一步采用轻量级微调策略，降低过拟合风险，同时保留语言模型的零样本学习效能。实验结果凸显了本模型在实现显著跨数据集零样本迁移能力方面的有效性，为图基础模型的发展开辟了路径。代码与数据可在https://github.com/NineAbyss/ZeroG获取。
英文摘要：
With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches of CV models like CLIP, both of which effectively bridge the gap between seen and unseen data. In the realm of graph learning, the continuous emergence of new graphs and the challenges of human labeling also amplify the necessity for zero-shot transfer learning, driving the exploration of approaches that can generalize across diverse graph data without necessitating dataset-specific and label-specific fine-tuning. In this study, we extend such paradigms to zero-shot transferability in graphs by introducing ZeroG, a new framework tailored to enable cross-dataset generalization. Addressing the inherent challenges such as feature misalignment, mismatched label spaces, and negative transfer, we leverage a language model to encode both node attributes and class semantics, ensuring consistent feature dimensions across datasets. We also propose a prompt-based subgraph sampling module that enriches the semantic information and structure information of extracted subgraphs using prompting nodes and neighborhood aggregation, respectively. We further adopt a lightweight fine-tuning strategy that reduces the risk of overfitting and maintains the zero-shot learning efficacy of the language model. The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models. Codes and data are available at https://github.com/NineAbyss/ZeroG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
