Network-Oriented Dynamics and Data Science (NODDS) Lab

About the lab

Network-Oriented Dynamics and Data Science Lab's main goal lies around theoretical and computational aspects of complex systems, mainly focusing on dynamics on complex networks and time series analysis by using tools from the fields of dynamical systems, statistical and computational physics, big data analysis and machine learning.
Our research interest is broad and multidisciplinary, includes Neuroscience, Climate Science and Powergrids.

Featured research (2)

Identifying and characterising dynamical regime shifts, critical transitions or potential tipping points in palaeoclimate time series is relevant for improving the understanding of often highly nonlinear Earth system dynamics. Beyond linear changes in time series properties such as mean, variance, or trend, these nonlinear regime shifts can manifest as changes in signal predictability, regularity, complexity, or higher-order stochastic properties such as multi-stability.In recent years, several classes of methods have been put forward to study these critical transitions in time series data that are based on concepts from nonlinear dynamics, complex systems science, information theory, and stochastic analysis. These includeapproaches such as phase space-based recurrence plots and recurrence networks, visibility graphs, order pattern-based entropies, and stochastic modelling.Here, we review and compare in detail several prominent methods from these fields by applying them to the same set of marine palaeoclimate proxy records of African climate variations during the past 5~million years. Applying these methods, we observe notable nonlinear transitions in palaeoclimate dynamics in these marine proxy records and discuss them in the context of important climate events and regimes such as phases of intensified Walker circulation, marine isotope stage M2, the onset of northern hemisphere glaciation and the mid-Pleistocene transition. We find that the studied approaches complement each other by allowing us to point out distinct aspects of dynamical regime shifts in palaeoclimate time series.We also detect significant correlations of these nonlinear regime shift indicators with variations of Earth's orbit, suggesting the latter as potential triggers of nonlinear transitions in palaeoclimate.Overall, the presented study underlines the potentials of nonlinear time series analysis approaches to provide complementary information on dynamical regime shifts in palaeoclimate and their driving processes that cannot be revealed by linear statistics or eyeball inspection of the data alone.
The identification of recurrences at various time scales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyse extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in USA, and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method.

Lab head

Deniz Eroglu
About Deniz Eroglu
  • Deniz Eroglu currently works at Kadir Has University, Istanbul, Turkey. Runs Network-Oriented Dynamics and Data Science (NODDS) Research Group. His main research focus is Dynamics on Complex Networks and Time Series Analysis.

Members (8)

Elif Yunt
  • Kadir Has University
İrem Topal Kement
  • Kadir Has University
Sajjad Bakrani
  • Imperial College London
Emre Kaya
  • Kadir Has University
Mustafa Küçük
  • Kadir Has University
Mehmet Kırtışoğlu
  • Bilkent University
Narcicegi Kiran
  • Mimar Sinan Fine Arts University
Toprak Firat
  • Kadir Has University