EventAsst. Prof. Deniz Eroğlu
Effective Networks: Predicting Network Structure and Critical Transitions from Data
Real-world complex systems such as ecosystems and neuron networks appear in most aspects of our everyday life. These complex systems are often made up of components, called nodes, which interact through an intricate network. By observing past behavior of such complex systems, it may be possible to predict behavior for some time in the future. However, it is much harder to predict new behavior of such complex systems when parameters change to a new range. In this talk, I will address this challenge by building an effective network, that is, a faithful model of the network consisting of the underlying local dynamics at each node and an accurate statistical description of the interactions. An effective network makes it possible to predict sudden changes in behavior – also known as critical transitions – that can lead to major disruptions in the complex system. The construct of an effective network only requires observations of the states of a representative sample of nodes for a relatively short time window. To illustrate the power of this approach, we show how to reconstruct the dynamics and structure of real networks, such as neuronal interactions in the cat cerebral cortex. In such network we were even able to predict critical transitions for parameters outside the observed range. These findings raise the possibility of network control to anticipate malfunctions in advance of sudden changes in behavior.
About The Speaker
Deniz Eroğlu is an Assist. Prof. at Kadir Has University. After graduating in physics (BA & MS) from Ege University in 2013, Eroğlu studied at Potsdam Institute for Climate Impact Research as a research associate in a project supported by Leibniz Association. He did his doctoral work in theoretical physics at Humboldt University Berlin and defended his thesis in January 2016 with summa cum laude. Following postdoctoral fellowships at Mathematics Department of Imperial College and Physics & Astronomy Department of Northwestern University he joined Kadir Has University in February 2019.
Deniz Eroğlu’s research interest is broad and multidisciplinary, encompassing nonlinear dynamics, network theory, complex systems and game theory. He started his career focusing on nonlinear dynamics and chaos applied to network science. These projects dealt with the collective behavior of coupled chaotic systems. In PhD time, he worked on time series analysis in climate systems to reveal hidden connections between monsoon regions. Identification of the long-term anti-correlation between monsoon activities in Northern and Southern parts of the globe brought him the best PhD thesis prize in Potsdam Institute for Climate Impact Research. Currently, he is involved in four major grants and working on several applications of interacting systems, including neural, climate and energy networks. His expertise lies at the intersection of dynamical systems and data analysis