Network Structure Identification from Corrupt Data Streams
Complex networked systems can be modeled as graphs with nodes representing the agents and links describing the dynamic coupling between them. Previous work on network identification has shown that the network structure of linear time-invariant (LTI) systems can be reconstructed from the joint power spectrum of the data streams. These results assumed that data is perfectly measured. However, real-world data is subject to many corruptions, such as inaccurate time-stamps, noise, and data loss. We show that identifying the structure of linear time-invariant systems using corrupt measurements results in the inference of erroneous links. We provide an exact characterization and prove that such erroneous links are restricted to the neighborhood of the perturbed node. We extend the analysis of LTI systems to the case of Markov random fields with corrupt measurements. We show that data corruption in Markov random fields results in spurious probabilistic relationships in precisely the locations where spurious links arise in LTI systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Network IdentificationSimilar Papers 제목 키워드 기반
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning
Since satellite systems are playing an increasingly important role in our civilization, their security and privacy weaknesses are more and more concerned. For example, prior work demonstrates that the communication chann…
Contrastive LearningData AugmentationDecoderWatch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring
This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directio…
Audio-Visual Speech Recognitionspeech-recognitionSpeech RecognitionVisual Speech RecognitionByteAction: Byte-space Action Recognition Foundation Model
Byte-space Action Recognition (BAR) aims to recognize human actions directly from compressed image bitstreams without any pixel decoding. By operating entirely in byte space, BAR is inherently independent of file integri…
Action RecognitionNTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results
This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from corrupted bitstreams, whose decoding oft…
Video RestorationApplications of the Streaming Networks
Most recently Streaming Networks (STnets) have been introduced as a mechanism of robust noise-corrupted images classification. STnets is a family of convolutional neural networks, which consists of multiple neural networ…
ClassificationGeneral Classificationimage-classificationImage Classification