Eroğlu, Deniz
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Deniz, Eroglu
Eroglu,D.
Eroğlu, DENIZ
DENIZ EROĞLU
Eroglu, Deniz
Eroglu D.
EROĞLU, DENIZ
Eroğlu,D.
Deniz EROĞLU
Eroğlu, D.
Eroğlu, Deniz
Deniz Eroğlu
E., Deniz
EROĞLU, Deniz
E.,Deniz
D. Eroğlu
Eroglu,Deniz
Eroğlu, Deniz
Eroglu,D.
Eroğlu, DENIZ
DENIZ EROĞLU
Eroglu, Deniz
Eroglu D.
EROĞLU, DENIZ
Eroğlu,D.
Deniz EROĞLU
Eroğlu, D.
Eroğlu, Deniz
Deniz Eroğlu
E., Deniz
EROĞLU, Deniz
E.,Deniz
D. Eroğlu
Eroglu,Deniz
Eroğlu, Deniz
Job Title
Dr. Öğr. Üyesi
Email Address
Main Affiliation
Molecular Biology and Genetics
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
15
LIFE ON LAND
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
14
LIFE BELOW WATER
1
Research Products
6
CLEAN WATER AND SANITATION
0
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3
GOOD HEALTH AND WELL-BEING
2
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
4
QUALITY EDUCATION
0
Research Products
2
ZERO HUNGER
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
13
CLIMATE ACTION
1
Research Products
1
NO POVERTY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
3
Research Products
5
GENDER EQUALITY
0
Research Products
Documents
36
Citations
726
h-index
16
This researcher does not have a WoS ID.
Scholarly Output
27
Articles
21
Views / Downloads
252/2237
Supervised MSc Theses
3
Supervised PhD Theses
3
WoS Citation Count
193
Scopus Citation Count
214
WoS h-index
8
Scopus h-index
8
Patents
0
Projects
0
WoS Citations per Publication
7.15
Scopus Citations per Publication
7.93
Open Access Source
20
Supervised Theses
6
| Journal | Count |
|---|---|
| Physical Review Research | 2 |
| Physical Review X | 2 |
| Earth Surface Processes and Landforms | 1 |
| Entropy | 1 |
| European Physical Journal-Special Topics | 1 |
Current Page: 1 / 4
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27 results
Scholarly Output Search Results
Now showing 1 - 10 of 27
Article Citation - WoS: 28Citation - Scopus: 26Network Structural Origin of Instabilities in Large Complex Systems(Amer Assoc Advancement Science, 2022) Duan, Chao; Nishikawa, Takashi; Eroglu, Deniz; Motter, Adilson E.A central issue in the study of large complex network systems, such as power grids, financial networks, and ecological systems, is to understand their response to dynamical perturbations. Recent studies recognize that many real networks show nonnormality and that nonnormality can give rise to reactivity-the capacity of a linearly stable system to amplify its response to perturbations, oftentimes exciting nonlinear instabilities. Here, we identify network structural properties underlying the pervasiveness of nonnormality and reactivity in real directed networks, which we establish using the most extensive dataset of such networks studied in this context to date. The identified properties are imbalances between incoming and outgoing network links and paths at each node. On the basis of this characterization, we develop a theory that quantitatively predicts nonnormality and reactivity and explains the observed pervasiveness. We suggest that these results can be used to design, upgrade, control, and manage networks to avoid or promote network instabilities.Master Thesis The Effect of Link Modifications on Network Synchronization(Kadir Has Üniversitesi, 2022) KIRAN, NARÇİÇEĞİ; Eroglu, DenizA major issue in studying complex network systems, such as neuroscience and power grids, is understanding the response of network dynamics to link modifications. The notion of network G(G, f, H) refers to di↵usively coupled identical oscillators, where isolated dynamics are chosen to be chaotic. As a consequence of the di↵usive nature, a globally synchronized state emerges as an invariant synchronization subspace, and it will be locally stable above critical coupling strength. Furthermore, the real part of the second minimum eigenvalue of the Laplacian matrix is inverse proportional to the critical coupling strength. Thus, we can use it to determine the synchronizability between two networks. Due to the asymmetry of the Laplacian matrix of a directed graph, adding directed links might cause a decrease in the real part of the second minimum eigenvalue of the Laplacian. If, after adding a link to a graph in a given network, the real part of the second minimum eigenvalue of the Laplacian matrix increases, it is called the enhancement of synchronization. Otherwise, it is called the hindrance of synchronization. In this research, we explore how the stability of synchronization at di↵usively coupled oscillators is a↵ected by link modifications for the networks created using particular motifs, i.e., cycle and star motifs. We consider a weakly connected directed graph consisting of two strongly connected components connected by directed link(s) (called cutset). We study the synchronization transitions in such networks when new directed link(s) between the components, in the opposite direction of the cutset, is added and strongly connects the whole network. We explore which properties of underlying graphs and their connected components may hinder or enhance the synchronization.Article Citation - Scopus: 7Collective Dynamics of Random Janus Oscillator Networks(American Physical Society, 2020) Peron,T.; Eroglu,D.; Rodrigues,F.A.; Moreno,Y.Janus oscillators have been recently introduced as a remarkably simple phase oscillator model that exhibits nontrivial dynamical patterns-such as chimeras, explosive transitions, and asymmetry-induced synchronization-that were once observed only in specifically tailored models. Here we study ensembles of Janus oscillators coupled on large homogeneous and heterogeneous networks. By virtue of the Ott-Antonsen reduction scheme, we find that the rich dynamics of Janus oscillators persists in the thermodynamic limit of random regular, Erdos-Rényi, and scale-free random networks. We uncover for all these networks the coexistence between partially synchronized states and a multitude of solutions of a collective state we denominate as a breathing standing wave, which displays global oscillations. Furthermore, abrupt transitions of the global and local order parameters are observed for all topologies considered. Interestingly, only for scale-free networks, it is found that states displaying global oscillations vanish in the thermodynamic limit. © 2020 authors. Published by the American Physical Society.Article Citation - WoS: 4Citation - Scopus: 4First-Principle Validation of Fourier's Law in D=1, 2, 3 Classical Systems(Elsevier, 2023) Tsallis, Constantino; Lima, Henrique Santos; Tirnakli, Ugur; Eroglu, DenizWe numerically study the thermal transport in the classical inertial nearest-neighbor XY ferromagnet in d = 1, 2, 3, the total number of sites being given by N = Ld, where L is the linear size of the system. For the thermal conductance sigma, we obtain sigma(T, L)L delta(d)= A(d) e-B(d) [L gamma (d)T ]eta(d) (with ez q(d) q equivalent to [1+(1-q)z]1/(1-q); ez1 = ez; A(d) > 0; B(d) > 0; q(d) > 1; eta(d) > 2; delta >= 0; gamma(d) > 0), for all values of L gamma(d)T for d = 1, 2, 3. In the L -> infinity limit, we have sigma proportional to 1/L rho sigma(d) with rho sigma(d) = delta(d)+gamma(d)eta(d)/[q(d)-1]. The material conductivity is given by kappa = sigma Ld proportional to 1/L rho kappa(d) (L -> infinity) with rho kappa(d) = rho sigma(d) - d. Our numerical results are consistent with 'conspiratory' d-dependences of (q, eta, delta, gamma), which comply with normal thermal conductivity (Fourier law) for all dimensions.(c) 2023 Published by Elsevier B.V.Master Thesis Makroskopik Ağ Sistemlerinde Veri Odaklı Trafik Akışı Modellemesi(2025) Fırat, Toprak; Eroğlu, DenizKentsel trafik sıkışıklığı, günümüz şehirleri için süregelen, karmaşık ve yüksek maliyetli bir problemdir. Artan seyahat süreleri, çevresel bozulma, enerji israfı ve ekonomik kayıplar bu problemin doğrudan sonuçları arasında yer almaktadır. Bu sorunlarla etkili şekilde başa çıkabilmek yalnızca altyapı yatırımlarıyla değil; aynı zamanda ulaşım politikaları, trafik yönetimi ve kontrol sistemlerinin bilimsel temellerle tasarlanmasıyla mümkündür. Bu kapsamda, trafiğin zaman ve mekân içinde nasıl evrildiğine dair sistematik ve ölçeklenebilir bir anlayış geliştirmek kritik önem taşır. Gerçek dünyada yapılacak deneyler genellikle maliyetli, zaman alıcı ve bozucudur. Bu nedenle, kentsel trafik sistemlerinin modellenmesi; alternatif senaryoların test edilmesi, politika etkilerinin değerlendirilmesi ve uzun vadeli sonuçların öngörülebilmesi açısından vazgeçilmez bir araçtır. Bununla birlikte, mevcut trafik modelleme yaklaşımları önemli sınırlılıklar taşır. Mikroskobik modeller bireysel araç davranışlarını yüksek ayrıntıyla temsil etse de, büyük ağlarda hesaplama açısından verimsizdir ve yoğun kalibrasyon verisi gerektirir. Makroskobik modeller ise daha hesaplıdır; ancak sabit başlangıç-varış (OD) akışları, homojen yol davranışları ve sürekli akış varsayımları gibi sadeleştirici kabuller içerir. Bu da onları karmaşık ve heterojen şehir yapıları için yetersiz kılar. Bu tez, trafik akışını yönlü bir ağda ayrık zamanlı yük alışverişiyle temsil eden veri odaklı bir makroskobik model önermektedir. Yol türlerine özgü akış dinamikleri, ağ topolojisi ve gözlemlenen trafik yoğunlukları modele entegre edilerek darboğazlar, geri tepme ve yük yeniden dağılımı gibi olgular temsil edilmektedir. Model parametreleri, evrimsel optimizasyon yoluyla, örtük talep varsayımı olmadan veriden öğrenilmektedir. Model, klasik Hücresel İletim Modeli (CTM) ile karşılaştırılmış; SUMO simülasyonları ve İstanbul, Londra ile New York verileri üzerinde üstünlük göstermiştir.Article Citation - WoS: 7Citation - Scopus: 8Sampling Rate-Corrected Analysis of Irregularly Sampled Time Series(Amer Physical Soc, 2022) Braun, Tobias; Fernandez, Cinthya N.; Eroglu, Deniz; Hartland, Adam; Breitenbach, Sebastian F. M.; Marwan, NorbertThe analysis of irregularly sampled time series remains a challenging task requiring methods that account for continuous and abrupt changes of sampling resolution without introducing additional biases. The edit distance is an effective metric to quantitatively compare time series segments of unequal length by computing the cost of transforming one segment into the other. We show that transformation costs generally exhibit a nontrivial relationship with local sampling rate. If the sampling resolution undergoes strong variations, this effect impedes unbiased comparison between different time episodes. We study the impact of this effect on recurrence quantification analysis, a framework that is well suited for identifying regime shifts in nonlinear time series. A constrained randomization approach is put forward to correct for the biased recurrence quantification measures. This strategy involves the generation of a type of time series and time axis surrogates which we call sampling-rate-constrained (SRC) surrogates. We demonstrate the effectiveness of the proposed approach with a synthetic example and an irregularly sampled speleothem proxy record from Niue island in the central tropical Pacific. Application of the proposed correction scheme identifies a spurious transition that is solely imposed by an abrupt shift in sampling rate and uncovers periods of reduced seasonal rainfall predictability associated with enhanced El Nino-Southern Oscillation and tropical cyclone activity.Article Network Dynamics Reconstruction From Data(Scıentıfıc Technıcal Research Councıl Turkey-Tubıtak, 2020) Eroğlu, DenizWe consider the problem of recovering the model of a complex network of interacting dynamical units from time series of observations. We focus on typical networks which exhibit heterogeneous degrees, i.e. where the number of connections varies widely across the network, and the coupling strength for a single interaction is small. In these networks, the behavior of each unit varies according to their connectivity. Under these mild assumptions, our method provides an effective network reconstruction of the network dynamics. The method is robust to a certain size of noise and only requires relatively short time series on the state variable of most nodes to determine: how well-connected a particular node is, the distribution of the nodes' degrees in the network, and the underlying dynamics.Article Citation - WoS: 19Citation - Scopus: 20Recurrence analysis of extreme event-like data(COPERNICUS GESELLSCHAFT MBH, 2021) Banerjee, Abhirup; Goswami, Bedartha; Hirata, Yoshito; Eroğlu, Deniz; Merz, Bruno; Kurths, Juergen; Marwan, NorbertThe 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 analyze an 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 the 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.Article Citation - WoS: 6Citation - Scopus: 6Transformation Cost Spectrum for Irregularly Sampled Time Series(Springer Heidelberg, 2023) Ozdes, Celik; Eroglu, DenizIrregularly sampled time series analysis is a common problem in various disciplines. Since conventional methods are not directly applicable to irregularly sampled time series, a common interpolation approach is used; however, this causes data distortion and consequently biases further analyses. We propose a method that yields a regularly sampled time series spectrum of costs with minimum information loss. Each time series in this spectrum is a stationary series and acts as a difference filter. The transformation costs approach derives the differences between consecutive and arbitrarily sized segments. After obtaining regular sampling, recurrence plot analysis is performed to distinguish regime transitions. The approach is applied to a prototypical model to validate its performance and to different palaeoclimate proxy data sets located around Africa to identify critical climate transition periods during the last 5 million years and their characteristic properties.Article Data-Driven Modeling of Traffic Flow in Macroscopic Network Systems(AIP Publishing, 2025) Firat, Toprak; Eroglu, DenizUrban traffic modeling is essential for understanding and mitigating congestion, yet existing approaches face a trade-off between realism and scalability. Microscopic agent-based simulators capture individual vehicle behavior but are computationally intensive and hard to calibrate at scale. Macroscopic models, while more efficient, often rely on strong assumptions, such as fixed origin-destination flows, or oversimplify network dynamics. In this work, we propose a data-driven macroscopic model that simulates traffic as a discrete-time load-exchange process over flow networks. The model captures key phenomena such as bottlenecks, spillbacks, and adaptive load redistribution using only road-type attributes, network structure, and observed traffic density. Parameter learning is performed via evolutionary optimization, allowing the model to adapt to both synthetic and real-world conditions without assuming latent travel demand. We evaluate the framework on synthetic grid-like networks and on real traffic data from London, Istanbul, and New York. The resulting framework provides a scalable and interpretable alternative for urban traffic forecasting, balancing predictive accuracy with computational efficiency across diverse network conditions.
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