AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control

AI 中文解读

1. 核心亮点：科学家让图中的每个节点都拥有了“自主决策权”，不再被动接受固定的信息传播规则，就像给每个节点装上了独立的小脑袋。 2. 通俗解读：想象一张巨大的社交网络图，每个用户就是一个节点，节点之间有各种连接关系。以前的研究者会给整张图设定一套固定的信息传递方式，比如“所有信息都沿着最短路径走”，但这并不适合所有节点——有的节点喜欢快速获取热门消息，有的节点则更关注小众圈子。现在提出的AgentGFM方法，让每个节点都能自己决定：从谁那里接收信息、选择哪条通道、何时停止接收。它们会先预测信息流向，然后观察实际结果，如果和自己想的不一样，就自动调整策略，不断优化自己的信息处理方式。 3. 实际影响：这项技术能显著提升推荐系统、社交网络分析、药物分子设计等领域的表现。比如你在购物平台上看到的商品推荐会更精准，因为模型能根据你的“节点”特性，动态调整信息传播路径，而不是对所有用户套用同一套规则。在生物医学领域，它能更有效地分析蛋白质相互作用网络，帮助发现新的药物靶点。普通人最直接的感受就是：各类智能服务变得更懂你的真实需求，错误推荐和无关信息会大大减少。

arXiv:2607.26533v1 Announce Type: new Abstract: 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.