复杂网络常通过若干选定的图论度量（如度、聚类系数或介数中心性）进行比较，这些度量可反映从局部到全局范围内的特定性质。本文提出一种基于 graphlets 的结构指纹框架：graphlets 是小规模的有根子图，其分布可系统刻画从局部到中观尺度的拓扑结构。在由多种随机图模型生成的合成网络上，graphlet 指纹能捕捉参数依赖的结构性差异，性能优于标准图论度量，并能识别出驱动判别任务的细微局部模式。随后，我们将该框架应用于实证的静息态功能连接组数据，发现尽管 graphlets 对受控的脑连接拓扑扰动亦表现出更高的敏感性，但在精神分裂症患者与健康对照的分类任务中，其性能仅与经典图论特征相当。这一结果支持如下观点：精神分裂症相关改变主要由空间局域化的连接变化主导，而非整体拓扑结构的重组。综上，生成式建模、靶向扰动实验及真实神经影像分类任务共同表明，graphlets 可作为复杂网络灵活的结构指纹，同时也严谨界定了其相较于经典图论特征的优势与局限。

Complex networks are often compared using selected graph-theoretical measures that capture a selected set of properties with effects ranging from local to global, such as degree, clustering or betweenness centrality. Here we introduce a structural fingerprinting framework based on graphlets: small rooted subgraphs whose distributions provide a systematic description of local-to-mesoscale topology. Across synthetic networks generated from several random graph models, graphlet fingerprints capture parameter-dependent structural differences, outperform standard graph-theoretical measures, and identify even subtle local patterns driving discrimination. We then apply the framework to empirical resting-state functional connectomes, documenting that while graphlets show superior sensitivity also to controlled topological perturbations of brain connectivity, specifically in schizophrenia-control classification they perform only comparably to classical graph-theoretical features. This is in line with the notion that schizophrenia-related alterations are dominated by spatially localized connectivity changes rather than general topological reorganization. Altogether, the generative modeling, targeted perturbations and real-world neuroimaging classification challenge position graphlets as flexible structural fingerprints of complex networks, while carefully outlining their strength and weaknesses compared to more classical graph theoretical features.