Graphs are fundamental mathematical structures used in various fields to model statistical and physical relationships between data, signals, and processes. In some applications, such as data processing in graphs that represent physical networks, the initial network topology is known. However, disconnections of edges in the network change the topology and may affect the signals and processes over the network. In this paper, we consider the problem of edge disconnection identification in networks by using concepts from graph signal processing (GSP). We assume that the graph signals measured over the vertices of the network can be represented as white noise that has been filtered on the graph topology by a smooth graph filter. We develop the likelihood ratio test (LRT) to detect a specific set of edge disconnections. Then, we provide the maximum likelihood (ML) decision rule for identifying general scenarios of edge disconnections in the network. It is shown that the sufficient statistics of the LRT and ML decision rule are the graph frequency energy levels in the graph spectral domain. However, the ML decision rule leads to a high-complexity exhaustive search over the edges in the network and is practically infeasible. Thus, we propose a low-complexity greedy method that identifies a single disconnected edge at each iteration. Moreover, by using the smoothness of the considered graph filter, we suggest a local implementation of the decision rule, which is based solely on the measurements at neighboring vertices. Simulation results demonstrate that the proposed methods outperform existing detection and identification methods on a synthetic dataset and for line outage identification in power systems from the IEEE 118-bus test case.
Prony, Padé, and Linear Prediction for Interpolation and Approximation in the Time and Frequency Domain Design of IIR Digital Filters and in Parameter Identification
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
Directed Graph Topology Inference via Graph Filter Identification
NVIDIA；Dept. of Signal Theory and Comms.（信号理论与通信系）；King Juan Carlos University（胡安卡洛斯国王大学）；Dept. of Electrical and Computer Eng.（电气与计算机工程系）；Rice University（里士满大学）；University of Rochester（罗切斯特大学）
Gaussian-trigonometric functional link artificial neural network: design and analysis
Sichuan University（四川大学）；Southwest University of Science and Technology（西南科技大学）；Hydrogen Energy and Multi-Energy Complementary Microgrid Engineering Technology Research Center of Sichuan Province（四川省氢能及多能互补微网工程技术研究中心）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Pontifical Catholic University of Rio de Janeiro (PUC-Rio)（里约热内卢天主教大学）
