Investigation of Stationarity for Graph Time Series Data Sets

Güneyi, Eylem Tuğçe
Vural, Elif
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.
2020 28th Signal Processing and Communications Applications Conference (SIU)


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...
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...
Approximation of pattern transformation manifolds with parametric dictionaries
Vural, Elif (2011-07-12)
The construction of low-dimensional models explaining high-dimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern transformation manifold models generated by in-plane geometric transformations of 2D visual patterns. We propose a method for computing a manifold by building a representative pattern such that its transformation manifold accurately fits a set of given observations. We present a solution for the progressive construction of the repr...
Analysis of extended feature models with constraint programming
Karataş, Ahmet Serkan; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2010)
In this dissertation we lay the groundwork of automated analysis of extended feature models with constraint programming. Among different proposals, feature modeling has proven to be very effective for modeling and managing variability in Software Product Lines. However, industrial experiences showed that feature models often grow too large with hundreds of features and complex cross-tree relationships, which necessitates automated analysis support. To address this issue we present a mapping from extended fe...
Analysis of correlated circular and extremal data with a flexible cylindrical distribution
Kalaylıoğlu Akyıldız, Zeynep Işıl (2021-08-01)
In this article, we introduce a flexible cylindrical distribution for modeling and analysis of dependent extremal and directional observations. The distribution can be used to investigate the connection between two related phenomena, such as the daily fastest wind speed and its direction. The proposed model is applicable for the analysis of a wide variety of cylindrical data, including datasets with asymmetrically distributed directional observations. The model enjoys the advantages of interpretable model p...
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: