Abstract
Abstract. Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős–Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970–2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.
Highlights
Networks are practical representations for complex systems with interacting components and have been used to study phenomena in sociology, engineering and natural systems (Barthélemy, 2011; Menck and Kurths, 2012; Palla et al, 2005; Holme et al, 2004)
In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure
Complex network techniques, based on statistical associations between climate parameter time series at different points on Earth, have yielded new insights in the investigation of climate dynamics (Tsonis and Swanson, 2008; Donges et al, 2009; Paluš et al, 2011). Such climate networks have been used for detecting longrange correlations, or teleconnections (Martin et al, 2013; Barreiro et al, 2011), and studying such phenomena such as the El Niño/Southern Oscillation (ENSO, Gozolchiani et al, 2008; Deza et al, 2013) and the Indian Monsoon system (Rehfeld et al, 2013; Malik et al, 2011; Stolbova et al, 2014)
Summary
Networks are practical representations for complex systems with interacting components and have been used to study phenomena in sociology, engineering and natural systems (Barthélemy, 2011; Menck and Kurths, 2012; Palla et al, 2005; Holme et al, 2004). Complex network techniques, based on statistical associations between climate parameter time series at different points on Earth, have yielded new insights in the investigation of climate dynamics (Tsonis and Swanson, 2008; Donges et al, 2009; Paluš et al, 2011) Such climate networks have been used for detecting longrange correlations, or teleconnections (Martin et al, 2013; Barreiro et al, 2011), and studying such phenomena such as the El Niño/Southern Oscillation (ENSO, Gozolchiani et al, 2008; Deza et al, 2013) and the Indian Monsoon system (Rehfeld et al, 2013; Malik et al, 2011; Stolbova et al, 2014). We construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain and use the CCEF to characterize the temporal evolution in the monsoon system
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