用极弱文本监督让图神经网络学会语言，实现零样本跨类别泛化。
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
- 通过联合学习图提示与文本提示，将图直接嵌入大模型空间。
- 在少样本、多任务、跨领域设置下性能优于现有方法。
- 首个实现极弱文本监督下零样本分类的GNN原型，适合迁移学习研究者。
尽管对比语言-图像预训练（CLIP）已在大规模图文对上取得显著成功，但构建可迁移的图神经网络（GNN）仍面临标签数据稀缺、文本监督有限、下游任务层次不一以及领域间概念鸿沟等问题。本文提出一种多模态提示学习范式，仅需少量语义标注样本和极弱文本监督，即可有效适配预训练GNN至下游任务与数据。该范式通过同步学习图提示与文本提示，将图直接嵌入大型语言模型（LLM）的表示空间。实验表明，该方法在少样本、多任务层级和跨领域场景中表现优异。此外，我们构建了首个类CLIP的零样本分类原型，可在极弱文本监督下实现未见类别的泛化。代码已开源：https://github.com/Violet24K/Morpher。
原文摘要 · Abstract (English)
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Graph Neural Networks (GNNs) with CLIP pipeline is challenging because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and the conceptual gaps between domains. In this work, to address these issues, we propose a multi-modal prompt learning paradigm to effectively adapt pre-trained GNN to downstream tasks and data, given only a few semantically labeled samples, each with extremely weak text supervision. Our new paradigm embeds the graphs directly in the same space as the Large Language Models (LLMs) by learning both graph prompts and text prompts simultaneously. We demonstrate the superior performance of our paradigm in few-shot, multi-task-level, and cross-domain settings. Moreover, we build the first CLIP-style zero-shot classification prototype that can generalize GNNs to unseen classes with extremely weak text supervision. The code is available at https://github.com/Violet24K/Morpher.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。