发表机构
School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有图基础模型固定传播方案不适配节点多样结构模式的问题，提出AgentGFM，以节点为智能体通过预测-行动-观察-修正实现自适应信息流控制，实验验证其在多样图拓扑中的有效性。
AI 中文摘要
图基础模型（GFMs）旨在从多域图中学习可迁移知识并适配未见过的场景。作为图中关系语义的基本来源，拓扑模式的可迁移性长期以来是GFM研究的核心。然而，局部结构模式可能在不同图之间甚至同一图内的节点间存在差异。尽管存在这种结构差异，大多数现有GFM仍依赖人工设计的传播方案，并基本不变地应用于新图。这种固定方案可能不适合不同节点的多样结构模式，由此提出一个关键问题：每个节点能否自主决定信息应如何通过图传播？我们将这种能力称为信息流控制。受智能体技术最新进展的启发，我们将此问题建模为基于智能体的决策过程，并将每个节点视为一个智能体。据此，我们提出AgentGFM，其中所有节点智能体遵循共享的端到端可训练策略，而非使用独立模型。为实现自适应信息流控制，每个节点通过预测-行动-观察-修正过程与图交互：在行动阶段，节点做出三项决策：源接收、信号通道选择和感知增益的逐节点停止；将得到的观察结果与预测值比较，二者的差异用于修正节点状态并指导后续交互。在节点级、图级和大规模迁移场景下开展的大量实验，证明了AgentGFM在多样图拓扑中的有效性。
英文摘要
Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research. However, local structural patterns may vary across graphs and even among nodes within the same graph. Despite such structural variation, most existing GFMs rely on manually designed propagation schemes and apply them to new graphs largely unchanged. Such fixed schemes may not suit the diverse structural patterns of different nodes. This raises a key question: can each node autonomously determine how information should be propagated through the graph? We refer to this capability as information-flow control. Inspired by recent advances in agent technology, we formulate this problem as agent-based decision making and treat each node as an agent. Accordingly, we propose AgentGFM, in which all node agents follow a shared end-to-end trainable policy rather than using independent models. For adaptive information-flow control, each node interacts with the graph through a predict-act-observe-correct process. During the act stage, the node makes three decisions: source reception, signal-channel selection and gain-aware node-wise halting. The resulting observation is compared with the prediction and their discrepancy is used to correct the node state and guide subsequent interactions. Extensive experiments across node-level, graph-level and large-scale transfer scenarios demonstrate the effectiveness of AgentGFM across diverse graph topologies.
Comments13 pages, 5 figures
