通过几何特征分析形状图数据，区分网络结构与空间形态差异。
Feature-based morphological analysis of shape graph data
- 提取拓扑、几何与方向特征，满足关键不变性要求
- 在道路网、神经元轨迹等数据上实现分组比较与分类
- 适合研究生物结构或城市网络的形态差异者
本文提出并验证了一套用于统计分析形状图数据集的计算流程，即嵌入在2D或3D空间中的几何网络。与传统抽象图不同，本方法不仅关注连接结构的变化，还捕捉网络分支的几何差异。所提方法基于一组精心设计且明确的拓扑、几何与方向特征，具备关键不变性。利用该特征表示，实现了对形状图队列的分组比较、聚类与分类。在多个真实数据集（包括城市道路/街道网络、神经元轨迹和星形胶质细胞成像）上评估了该表示的有效性，并与多种基于特征及非特征的方法进行对比。
原文摘要 · Abstract (English)
This paper introduces and demonstrates a computational pipeline for the statistical analysis of shape graph datasets, namely geometric networks embedded in 2D or 3D spaces. Unlike traditional abstract graphs, our purpose is not only to retrieve and distinguish variations in the connectivity structure of the data but also geometric differences of the network branches. Our proposed approach relies on the extraction of a specifically curated and explicit set of topological, geometric and directional features, designed to satisfy key invariance properties. We leverage the resulting feature representation for tasks such as group comparison, clustering and classification on cohorts of shape graphs. The effectiveness of this representation is evaluated on several real-world datasets including urban road/street networks, neuronal traces and astrocyte imaging. These results are benchmarked against several alternative methods, both feature-based and not.
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