复杂网络上的扩散过程是模拟多种运输系统的便捷框架。网络链路故障可能引发级联现象或系统拥塞。实时检测此类故障可缓解其影响，并优化运输网络的控制策略。本研究的主要目标是为发生扩散动力学的运输网络提供一种降维技术，使其仅通过有限数量的观测即可检测故障的存在。该方法基于网络状态在链路权重随机扰动下的响应敏感性，并利用节点波动间的相关性实现聚类。由此引入各簇的“代表性节点”，构建降维后的网络模型；该简化模型的动力学状态可通过有限观测进行检测。我们进一步针对整个网络实现故障识别流程，通过分析粗粒化网络的动力学行为完成识别。所提聚类算法的故障定位效率（对所有可能的单边故障取平均）在不同图结构配置下与传统基于结构的聚类方法进行了比较。结果表明，该聚类算法对具有高稳态通量的链路故障的检测灵敏度高于传统聚类技术。

Diffusion on complex networks is a convenient framework to simulate a great variety of transport systems. The effects of failures in the network links may be used to cascade phenomena or the congestion formation in the system. A real time detection of failures can mitigate their effect and allow to optimize the control procedures on the transport network. The main objective of this work is to provide a dimensionality reduction technique for a transport network where a diffusive dynamics takes place, to detect presence of a failure by a limited number of observations. Our approach is based on the susceptibility response of the network state under random perturbations of the link weights. The correlations among the nodes fluctuations is exploited in order to provide the clustering procedure. The network dimensionality is therefore reduced introducing `representative nodes' for each cluster and generating a reduced network model, whose dynamical state is detected by the limited observations. We realize a failure identification procedure for the whole network, studying the dynamics of the coarse-grained network. The localization efficiency of the proposed clustering algorithm, averaging over all possible single-edge failures, is compared with traditional structure-based clustering using different graph configurations. We show that the proposed clustering algorithm is more sensitive than traditional clustering techniques to detect link failure with high stationary fluxes.