@article{DjurdjevacConradSarichSchuette2012, author = {Djurdjevac Conrad, Natasa and Sarich, Marco and Sch{\"u}tte, Christof}, title = {Estimating the eigenvalue error of Markov State Models}, volume = {10}, journal = {Multiscale Modeling \& Simulation}, number = {1}, doi = {10.1137/100798910}, pages = {61 -- 81}, year = {2012}, language = {en} } @inproceedings{DjurdjevacConradSarichSchuette2010, author = {Djurdjevac Conrad, Natasa and Sarich, Marco and Sch{\"u}tte, Christof}, title = {On Markov State Models for Metastable Processes}, booktitle = {Proceedings of the International Congress of Mathematics, Hyderabad, India, Section Invited Talks. (ICM) 2010}, year = {2010}, language = {en} } @article{DjurdjevacConradHelfmannZonkeretal.2018, author = {Djurdjevac Conrad, Natasa and Helfmann, Luzie and Zonker, Johannes and Winkelmann, Stefanie and Sch{\"u}tte, Christof}, title = {Human mobility and innovation spreading in ancient times: a stochastic agent-based simulation approach}, volume = {7}, journal = {EPJ Data Science}, number = {1}, edition = {EPJ Data Science}, publisher = {EPJ Data Science}, doi = {10.1140/epjds/s13688-018-0153-9}, pages = {24}, year = {2018}, abstract = {Human mobility always had a great influence on the spreading of cultural, social and technological ideas. Developing realistic models that allow for a better understanding, prediction and control of such coupled processes has gained a lot of attention in recent years. However, the modeling of spreading processes that happened in ancient times faces the additional challenge that available knowledge and data is often limited and sparse. In this paper, we present a new agent-based model for the spreading of innovations in the ancient world that is governed by human movements. Our model considers the diffusion of innovations on a spatial network that is changing in time, as the agents are changing their positions. Additionally, we propose a novel stochastic simulation approach to produce spatio-temporal realizations of the spreading process that are instructive for studying its dynamical properties and exploring how different influences affect its speed and spatial evolution.}, language = {en} } @article{BeckerDjurdjevacConradEseretal.2020, author = {Becker, Fabian and Djurdjevac Conrad, Natasa and Eser, Raphael A. and Helfmann, Luzie and Sch{\"u}tt, Brigitta and Sch{\"u}tte, Christof and Zonker, Johannes}, title = {The Furnace and the Goat—A spatio-temporal model of the fuelwood requirement for iron metallurgy on Elba Island, 4th century BCE to 2nd century CE}, volume = {15}, journal = {PLOS ONE}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0241133}, pages = {1 -- 37}, year = {2020}, language = {en} } @article{DjurdjevacConradFuerstenauGrabundzijaetal.2018, author = {Djurdjevac Conrad, Natasa and Fuerstenau, Daniel and Grabundzija, Ana and Helfmann, Luzie and Park, Martin and Schier, Wolfram and Sch{\"u}tt, Brigitta and Sch{\"u}tte, Christof and Weber, Marcus and Wulkow, Niklas and Zonker, Johannes}, title = {Mathematical modeling of the spreading of innovations in the ancient world}, volume = {7}, journal = {eTopoi. Journal for Ancient Studies}, issn = {ISSN 2192-2608}, doi = {10.17171/4-7-1}, year = {2018}, language = {en} } @article{ThielDjurdjevacConradNtinietal.2019, author = {Thiel, Denise and Djurdjevac Conrad, Natasa and Ntini, Evgenia and Peschutter, Ria and Siebert, Heike and Marsico, Annalisa}, title = {Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis}, volume = {20}, journal = {BMC Bioinformatics}, number = {1471-2105}, doi = {10.1186/s12859-019-2900-8}, year = {2019}, abstract = {Background: Although several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them have unknown function. Recent evidence has shown the importance of both lncRNAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of 'guilt-by-association', no strategy exists so far which identifies mRNA-lncRNA functional modules based on the 3D chromatin interaction graph. Results: To better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides in the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs. Conclusions: Interestingly, we find that lncRNA network hubs tend to be significantly enriched in evolutionary conserved lncRNAs and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we mine putative regulatory functions for uncharacterized lncRNAs.}, language = {en} } @article{BanischDjurdjevacConradSchuette2015, author = {Banisch, Ralf and Djurdjevac Conrad, Natasa and Sch{\"u}tte, Christof}, title = {Reactive flows and unproductive cycles for random walks on complex networks}, journal = {The European Physical Journal Special Topics, vol. 224, iss. 12 (2015) pp. 2369-2387}, doi = {10.1140/epjst/e2015-02417-8}, year = {2015}, language = {en} } @misc{DjurdjevacConradWeberSchuette2015, author = {Djurdjevac Conrad, Natasa and Weber, Marcus and Sch{\"u}tte, Christof}, title = {Finding dominant structures of nonreversible Markov processes}, issn = {1438-0064}, doi = {10.1137/15M1032272}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-55739}, year = {2015}, abstract = {Finding metastable sets as dominant structures of Markov processes has been shown to be especially useful in modeling interesting slow dynamics of various real world complex processes. Furthermore, coarse graining of such processes based on their dominant structures leads to better understanding and dimension reduction of observed systems. However, in many cases, e.g. for nonreversible Markov processes, dominant structures are often not formed by metastable sets but by important cycles or mixture of both. This paper aims at understanding and identifying these different types of dominant structures for reversible as well as nonreversible ergodic Markov processes. Our algorithmic approach generalizes spectral based methods for reversible process by using Schur decomposition techniques which can tackle also nonreversible cases. We illustrate the mathematical construction of our new approach by numerical experiments.}, language = {en} } @misc{BanischDjurdjevacConradSchuette2015, author = {Banisch, Ralf and Djurdjevac Conrad, Natasa and Sch{\"u}tte, Christof}, title = {Reactive flows and unproductive cycles for random walks on complex networks}, issn = {1438-0064}, doi = {10.1140/epjst/e2015-02417-8}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-54239}, year = {2015}, abstract = {We present a comprehensive theory for analysis and understanding of transition events between an initial set A and a target set B for general ergodic finite-state space Markov chains or jump processes, including random walks on networks as they occur, e.g., in Markov State Modelling in molecular dynamics. The theory allows us to decompose the probability flow generated by transition events between the sets A and B into the productive part that directly flows from A to B through reaction pathways and the unproductive part that runs in loops and is supported on cycles of the underlying network. It applies to random walks on directed networks and nonreversible Markov processes and can be seen as an extension of Transition Path Theory. Information on reaction pathways and unproductive cycles results from the stochastic cycle decomposition of the underlying network which also allows to compute their corresponding weight, thus characterizing completely which structure is used how often in transition events. The new theory is illustrated by an application to a Markov State Model resulting from weakly damped Langevin dynamics where the unproductive cycles are associated with periodic orbits of the underlying Hamiltonian dynamics.}, language = {en} } @misc{DjurdjevacConradBanischSchuette2014, author = {Djurdjevac Conrad, Natasa and Banisch, Ralf and Sch{\"u}tte, Christof}, title = {Modularity of Directed Networks: Cycle Decomposition Approach}, issn = {1438-0064}, doi = {10.3934/jcd.2015.2.1}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-51166}, year = {2014}, abstract = {The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number of fuzzy clustering methods for directed networks, many of these approaches tend to neglect important directional information. In this paper, we present a novel random walk based approach for finding fuzzy partitions of directed, weighted networks, where edge directions play a crucial role in defining how well nodes in a module are interconnected. We will show that cycle decomposition of a random walk process connects the notion of network modules and information transport in a network, leading to a new, symmetric measure of node communication. Finally, we will use this measure to introduce a communication graph, for which we will show that although being undirected it inherits all necessary information about modular structures from the original network.}, language = {en} } @article{DjurdjevacConradBanischSchuette2015, author = {Djurdjevac Conrad, Natasa and Banisch, Ralf and Sch{\"u}tte, Christof}, title = {Modularity of Directed Networks: Cycle Decomposition Approach}, journal = {Journal of Computational Dynamics 2 (2015) pp. 1-24}, doi = {10.3934/jcd.2015.2.1}, year = {2015}, abstract = {The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number of fuzzy clustering methods for directed networks, many of these approaches tend to neglect important directional information. In this paper, we present a novel random walk based approach for finding fuzzy partitions of directed, weighted networks, where edge directions play a crucial role in defining how well nodes in a module are interconnected. We will show that cycle decomposition of a random walk process connects the notion of network modules and information transport in a network, leading to a new, symmetric measure of node communication. Finally, we will use this measure to introduce a communication graph, for which we will show that although being undirected it inherits all necessary information about modular structures from the original network.}, language = {en} } @misc{BanischSchuetteDjurdjevacConrad2014, author = {Banisch, Ralf and Sch{\"u}tte, Christof and Djurdjevac Conrad, Natasa}, title = {Module Detection in Directed Real-World Networks}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-49849}, year = {2014}, abstract = {We investigate the problem of finding modules (or clusters, communities) in directed networks. Until now, most articles on this topic have been oriented towards finding complete network partitions despite the fact that this often is unwanted. We present a novel random walk based approach for non-complete partitions of the directed network into modules in which some nodes do not belong to only one of the modules but to several or to none at all. The new random walk process is reversible even for directed networks but inherits all necessary information about directions and structure of the original network. We demonstrate the performance of the new method in application to a real-world earthquake network.}, language = {en} } @article{DjurdjevacConradWeberSchuette2016, author = {Djurdjevac Conrad, Natasa and Weber, Marcus and Sch{\"u}tte, Christof}, title = {Finding dominant structures of nonreversible Markov processes}, volume = {14}, journal = {Multiscale Modeling and Simulation}, number = {4}, doi = {10.1137/15M1032272}, pages = {1319 -- 1340}, year = {2016}, language = {en} } @phdthesis{DjurdjevacConrad2012, author = {Djurdjevac Conrad, Natasa}, title = {Methods for analyzing complex networks using random walker}, year = {2012}, language = {en} } @article{BanischDjurdjevacConrad2014, author = {Banisch, Ralf and Djurdjevac Conrad, Natasa}, title = {Cycle-flow-based module detection in directed recurrence networks}, volume = {108}, journal = {EPL (Europhysics Letters)}, number = {6}, doi = {10.1209/0295-5075/108/68008}, year = {2014}, language = {en} } @article{LorenzWolfBraunetal.2017, author = {Lorenz, Philipp and Wolf, Frederik and Braun, Jonas and Djurdjevac Conrad, Natasa and H{\"o}vel, Philipp}, title = {Capturing the Dynamics of Hashtag-Communities}, journal = {International Workshop on Complex Networks and their Applications}, doi = {10.1007/978-3-319-72150-7_33}, pages = {401 -- 413}, year = {2017}, language = {en} } @misc{ZonkerPadillaIglesiasDjurdjevacConrad2023, author = {Zonker, Johannes and Padilla-Iglesias, Cecilia and Djurdjevac Conrad, Natasa}, title = {Supplementary code and data for Royal Society Open Science Manuscript rsos.230495}, doi = {10.12752/9254}, year = {2023}, abstract = {In this repository are all files necessary to run the agent-based model of the paper "Insights into drivers of mobility and cultural dynamics of African hunter-gatherers over the past 120 000 years", Royal Society Open Science, 10(11), 2023.}, language = {en} } @misc{HelfmannDjurdjevacConradLorenzSpreenetal.2023, author = {Helfmann, Luzie and Djurdjevac Conrad, Natasa and Lorenz-Spreen, Philipp and Sch{\"u}tte, Christof}, title = {Supplementary code for the paper Modelling opinion dynamics under the impact of influencer and media strategies}, doi = {10.12752/9267}, year = {2023}, abstract = {This repository contains the Julia code accompanying the paper "Modelling opinion dynamics under the impact of influencer and media strategies", Scientific Reports, Vol.13, p. 19375, 2023.}, language = {en} } @article{TrowerDjurdjevacConradKlus2025, author = {Trower, Maia and Djurdjevac Conrad, Natasa and Klus, Stefan}, title = {Clustering Time-Evolving Networks Using the Spatiotemporal Graph Laplacian}, volume = {35}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, arxiv = {http://arxiv.org/abs/2407.12864}, doi = {10.1063/5.0228419}, pages = {013126}, year = {2025}, abstract = {Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis (CCA) to capture the temporal evolution of clusters. Based on this extended canonical correlation framework, we define the spatio-temporal graph Laplacian and investigate its spectral properties. We connect these concepts to dynamical systems theory via transfer operators, and illustrate the advantages of our method on benchmark graphs by comparison with existing methods. We show that the spatio-temporal graph Laplacian allows for a clear interpretation of cluster structure evolution over time for directed and undirected graphs.}, language = {en} } @article{KlusDjurdjevacConrad2024, author = {Klus, Stefan and Djurdjevac Conrad, Natasa}, title = {Dynamical systems and complex networks: A Koopman operator perspective}, volume = {5}, journal = {Journal of Physics: Complexity}, number = {4}, publisher = {IOP Publishing}, arxiv = {http://arxiv.org/abs/2405.08940}, doi = {10.1088/2632-072X/ad9e60}, pages = {041001}, year = {2024}, abstract = {The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept-representing highly nonlinear dynamical systems by infinite-dimensional linear operators-has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.}, language = {en} } @article{DjurdjevacConradQuangVuNagel2024, author = {Djurdjevac Conrad, Natasa and Quang Vu, Nhu and Nagel, Soeren}, title = {Co-evolving networks for opinion and social dynamics in agent-based models}, volume = {34}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, number = {9}, issn = {1054-1500}, arxiv = {http://arxiv.org/abs/2407.00145}, doi = {10.1063/5.0226054}, year = {2024}, abstract = {The rise of digital social media has strengthened the coevolution of public opinions and social interactions, that shape social structures and collective outcomes in increasingly complex ways. Existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, like echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time.}, language = {en} }