近期，置换熵及其衍生方法向图信号的拓展为分析在复杂网络上演化的高维复杂系统开辟了新方向。然而，这些度量均根植于香农熵与符号动力学。本文首次探索并验证了经典基于条件熵的度量——样本熵（Sample Entropy, SampEn）是否可被有效定义于图信号之上，并用于刻画复杂网络中数据的非线性动力学特性。我们提出图信号样本熵（SampEnG），该框架通过基于多跳邻域的拓扑感知嵌入，在连续嵌入状态空间中计算有限尺度的相关和，从而将经典样本熵从一维与二维信号（包括时间序列与图像）统一推广至图信号。在合成数据集及真实世界数据集（包括气象站、无线传感器监测与交通系统）上的实验表明，SampEnG 能够复现路径与网格结构上已知的非线性动力学特征。在交通流分析中，针对有向拓扑（编码因果流约束）所计算的 SampEnG 对自由流与拥堵之间的相变尤为敏感，所提供的信息可补充现有基于香农熵的方法。我们预期 SampEnG 将为图信号分析提供新途径，将样本熵及条件熵的概念推广至各类网络数据的非线性分析中。

The recent extension of permutation entropy and its derivatives to graph signals has opened up new horizons for the analysis of complex, high-dimensional systems evolving on networks. However, these measures are all fundamentally rooted in Shannon entropy and symbol dynamics. In this paper, we explore, for the first time, whether and how a popular conditional-entropy based measure --Sample Entropy (SampEn)-- can be effectively defined for graph signals and used to characterise the nonlinear dynamics of data on complex networks. We introduce sample entropy for graph signals (SampEnG), a unified framework that generalises classical sample entropy from uni- and bi-dimensional signals, including time series and images, by building on topology-aware embeddings using multi-hop neighbourhoods and computing finite scale of correlation sums in the continuous embedding state space. Experiments on synthetic and real-world datasets, including weather station, wireless sensor monitoring, and traffic systems, verify that SampEnG recovers known nonlinear dynamical features on paths and grids. In the traffic-flow analysis, SampEnG on a directed topology (encoding causal flow constraint) is particularly sensitive to phase transitions between free-flow and congestion, offering information that is complementary to existing Shannon-entropy based approaches. We expect SampEnG to open up new ways to analyse graph signals, generalising sample entropy and the concept of conditional entropy to extending nonlinear analysis to a wide variety of network data.