发表机构
Institute of Computer Science of the Czech Academy of Sciences; National Institute of Mental Health; Computer Science Institute of Charles University, Faculty of Mathematics and Physics, Charles University（捷克科学院计算机科学研究所; 国家精神卫生研究所; 查理大学数学物理学院计算机科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于图元的复杂网络结构指纹框架，在合成网络和脑功能连接组中验证其敏感性，并指出在精神分裂症分类中与经典图论特征相当，强调局部连接变化的主导作用。
AI 中文摘要
复杂网络通常通过选定的图论度量进行比较，这些度量捕捉从局部到全局的一系列属性，例如度、聚类系数或介数中心性。在此，我们引入一种基于图元的结构指纹框架：图元是小的有根子图，其分布提供了从局部到中尺度拓扑的系统性描述。在由几种随机图模型生成的合成网络中，图元指纹捕捉到参数依赖的结构差异，优于标准图论度量，并能识别出驱动区分的微妙局部模式。随后，我们将该框架应用于经验静息态功能连接组，记录到虽然图元对脑连接性的受控拓扑扰动也表现出优越的敏感性，但在精神分裂症-对照分类中，其表现仅与经典图论特征相当。这与精神分裂症相关改变主要由空间局部的连接性变化而非整体拓扑重组主导的观点一致。总之，生成建模、定向扰动和真实世界神经影像分类挑战将图元定位为复杂网络的灵活结构指纹，同时仔细概述了它们与更经典图论特征相比的优缺点。
英文摘要
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.
