Graphlets as structural fingerprints of complex networks

2026-09-15

AI Summary

研究引入基于图元的结构指纹框架，以系统描述局部到中尺度拓扑，用于复杂网络比较，尤其在合成网络和脑功能连接组分类中表现优异。

Abstract

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.

Problem

• complex networks
• graph-theoretical measures
• structural fingerprints
• graphlets
• topological features

Innovation

• graphlets
• structural fingerprinting
• complex networks
• topological perturbations
• functional connectomes