让图神经网络像蚁群一样自组织，实现跨规模的同步控制与协作。
Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems
- 用带符号的注意力机制模拟节点间可学习的局部交互规则。
- 在未训练过的网络规模和动态下，仍能生成指定同步模式并加速运动模式切换。
- 适用于机器人协调、异质图分类等任务，适合做智能群体系统研究者参考。
集体行为源于局部交互单元产生协调的全局组织，如动力系统中的同步或图上的任务相关信息流。核心挑战不仅是解释其如何涌现，更是设计能生成期望全局结构且泛化于不同图结构、动力学和任务的局部交互规则。为此，我们提出蜂群启发的涌现同步器（SIES），一种图动力学框架，可学习可泛化的局部交互规律以实现可控的集体组织。每个节点为带有状态和任务提示的类代理动力学单元，有向源-目标条件注意力作为显式演化模型中的自适应耦合项。因此，SIES结合了显式动力引擎与局部智能，类似生物蜂群。在同步控制方面，SIES学习到的通用耦合算子可在未经训练的网络尺度、目标相位关系及节点内在动力学下生成预定同步模式，无需重训练；该算子比三种振子基线更快达到步态相关模式，并成功推广至不同尺度的模拟多足机器人及物理六足机器人（腿部失效后）。在图表示学习中，SIES将相同符号交互原则应用于消息传递，在异质节点分类基准上性能优于对比方法。这些结果表明，SIES是一种可泛化且可学习的图动力学交互框架，有望应用于同步控制、自适应机器人协作与异质图表示学习。
原文摘要 · Abstract (English)
Collective behavior arises when locally interacting units produce coordinated global organization, from synchronization in dynamical systems to task-relevant information flow on graphs. The central challenge is not only to explain how collective behavior emerges, but to design local interaction rules that can produce desired global organization and generalize across graphs, dynamics and tasks.To address this challenge, we introduce the Swarm-Inspired Emergent Synchronizer (SIES), a graph-dynamical framework that learns generalizable local-interaction laws for controllable collective organization. Each node is an agent-like dynamical unit with a state and task cue, and signed source-target-conditioned attention acts as an adaptive coupling term inside an explicit evolution model. Therefore, SIES combines an explicit dynamical engine with local agent intelligence, similar to biological swarms. For synchronization control, SIES learns a generalizable coupling operator that produces prescribed synchronization patterns for CDSs across untrained network scales, target phase relations, and intrinsic node dynamics without retraining. The learned operator also reaches gait-related modes faster than three oscillator baselines and generalizes synchronization-driven locomotion to simulated multi-legged robots of different scales and a physical hexapod after leg disablement. For graph representation learning, SIES applies the same signed interaction principle to message passing and achieves the highest performance among the compared methods on heterophilous node-classification benchmarks. Together, these results position SIES as a generalizable and learnable graph-dynamical interaction framework with promise for synchronization control, adaptive robot coordination, and heterophilous graph representation learning.
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