让每个节点自己做决策，提升图学习的适应性和全局理解能力。
Node-as-Agent: Graph Agentic Network
- 节点作为智能体自主规划消息传递路径，实现动态推理。
- 在少样本场景下仅用冻结LLM即达领先性能，无需微调。
- 结合检索增强生成，捕捉远距离语义关联，适合复杂图结构任务。
图神经网络通过预定义的聚合机制在邻近节点间传播信息，取得显著成果。然而，这类固定方案存在两大局限：一是无法处理节点信息量不平衡问题，部分节点信息丰富而其他节点稀疏；二是消息传递主要依赖局部结构相似性，忽视图中全局语义关系，限制了对远距离相关信息的捕捉能力。为此，我们提出检索增强型图智能体网络（ReaGAN），一种基于智能体的框架，赋予每个节点自主决策能力。每个节点作为独立智能体，依据自身记忆规划下一步行动，实现节点级规划与自适应消息传播。同时，检索增强生成（RAG）使节点可访问语义相关的外部内容，构建图中全局关系。ReaGAN在少样本上下文设置下，仅使用冻结的大型语言模型骨干网络即可实现竞争力表现，无需微调，展示了智能体规划与本地-全局检索在图学习中的潜力。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However, such fixed schemes often suffer from two key limitations. First, they cannot handle the imbalance in node informativeness -- some nodes are rich in information, while others remain sparse. Second, predefined message passing primarily leverages local structural similarity while ignoring global semantic relationships across the graph, limiting the model's ability to capture distant but relevant information. We propose Retrieval-augmented Graph Agentic Network (ReaGAN), an agent-based framework that empowers each node with autonomous, node-level decision-making. Each node acts as an agent that independently plans its next action based on its internal memory, enabling node-level planning and adaptive message propagation. Additionally, retrieval-augmented generation (RAG) allows nodes to access semantically relevant content and build global relationships in the graph. ReaGAN achieves competitive performance under few-shot in-context settings using a frozen LLM backbone without fine-tuning, showcasing the potential of agentic planning and local-global retrieval in graph learning.
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