Investigation of Stationarity for Graph Time Series Data Sets

Date

2021-01-07

Author

Güneyi, Eylem Tuğçe
Vural, Elif

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

343
views

0
downloads

Graphs permit the analysis of the relationships in complex data sets effectively. Stationarity is a feature that facilitates the analysis and processing of random time signals. Since graphs have an irregular structure, the definition of classical stationarity does not apply to graphs. In this study, we study how stationarity is defined for graph random processes and examine the validity of the stationarity assumption with experiments on synthetic and real data sets.

Subject Keywords

Graph signal processing, stationarity, time-vertex processes

URI

https://hdl.handle.net/11511/89406

DOI

https://doi.org/10.1109/siu49456.2020.9302376

Conference Name

2020 28th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme Acar, Abdullah Burak; Vural, Elif (2022-01-01) We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The parameters of the time-vertex kernels are optimized jointly with the sparse representation coefficients of the signals, so that the learnt representation fits well to the available observations of the time-vertex signals at hand. The missing observations of the signals are then estimated b...
ESTIMATION OF TIME VARYING GRAPH SIGNALS WITH GRAPH ARMA PROCESSES Güneyi, Eylem Tuğçe; Vural, Elif; Department of Electrical and Electronics Engineering (2021-9-8) Graph models provide efficient tools for analyzing data defined over irregular domains such as social networks, sensor networks, and transportation networks. Real-world graph signals are usually time-varying signals. The characterization of the joint behavior of time-varying graph signals in the time and the vertex domains has recently arisen as an interesting research problem, contrasted to the independent processing of graph signals acquired at different time instants. The concept of wide sense stationari...
Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations Guneyi, Eylem Tugce; Canbolat, Abdullah; Vural, Elif (2021-01-01) © 2021 IEEE.We consider the problem of estimating time-varying graph signals with missing observations, which is of interest in many applications involving data acquisition on irregular topologies. We model time-varying graph signals as jointly stationary time-vertex ARMA graph processes. We formulate the learning of ARMA process parameters as an optimization problem where the joint power spectral density of the model is fit to a rough empirical estimate of the process covariance matrix. We propose a convex...
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals Acar, Abdullah Burak; Vural, Elif (2022-01-01) In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying graph signals, and propose a solution for the estimation of the missing signal observations by learning time-vertex dictionaries from the available observations. We adopt a time-vertex dictionary model defined through a set of joint time-vertex spectral kernels, each of which captures a different spec...
A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...

Citation Formats

E. T. Güneyi and E. Vural, “Investigation of Stationarity for Graph Time Series Data Sets,” presented at the 2020 28th Signal Processing and Communications Applications Conference (SIU), 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89406.