Understanding how various collective phenomena emerge out of complex interactions is a challenge in multiple disciplines. To meet this challenge, networks, accounting for the structural connections and functional coherences between units, became a major research paradigm. Researches cover topics from fundamental theory to empirical analyses and engineering practice along with two main directions -- the global and local structure of networks, and their association with outcoming dynamical behavior at various levels across systems of distinct nature. From the topological perspective, for example, it has been shown that certain topological features can facilitate the signal transmission efficiency, meanwhile can be also robust to perturbations preventing cascading failure. These fundings then on the one hand help us to understand the high efficiency of brain networks, on the other hand, guide the design of communication networks and power grids. From the dynamical perspective, as another example, the firing pattern of neural networks is associated with the underlying connection networks, which are considered to be essential to understand the observed brain dynamics. The topological and dynamical perspectives are complementary, and joint investigation of these two is the key to deepening our understanding of the emergence of collective phenomena in various disciplines.
Despite the devoted efforts and fruitful achievements in the last decades, there are gaps that still need to be filled. In the fundamental theory, novel indexes are needed to better characterize different aspects of local and global features of networks; the association of network structure with collective system behaviors is not yet established; the impact of reorganization of network topology on resulting collective dynamics, e.g. synchronization and anomalous oscillations, has to be better quantified. Regarding the multi-disciplinary applications, we still lack methods to well and truly infer the complex underlying networks from complex observed data; there still exist difficulties in predicting outcoming behaviors of a complex system from its network topology and units dynamics; further, how to understand the working mechanisms of natural complex systems and how to design manmade complex systems according to modern network theory are still open questions.
This Research Topic aims to extend the research horizon, and collect articles with a focus on the fundamental theory of complex networks to multi-disciplinary applications of network theory. The editors of this Research Topic will guarantee a fast, but fair, peer-to-peer review procedure, to provide society with a reliable injection of scientific insights. High-quality Original Research and Review articles in this field are all welcome for submission to this Research Topic. Research interests include but are not limited to the following areas:
• Modeling of multiplex networks;
• Dynamical systems on networks;
• Network construction from data;
• Machine learning and big data analysis based on networks;
• Novel method to quantify network topology;
• Application of network theory to social systems, brain networks, power grids, and so on.
Understanding how various collective phenomena emerge out of complex interactions is a challenge in multiple disciplines. To meet this challenge, networks, accounting for the structural connections and functional coherences between units, became a major research paradigm. Researches cover topics from fundamental theory to empirical analyses and engineering practice along with two main directions -- the global and local structure of networks, and their association with outcoming dynamical behavior at various levels across systems of distinct nature. From the topological perspective, for example, it has been shown that certain topological features can facilitate the signal transmission efficiency, meanwhile can be also robust to perturbations preventing cascading failure. These fundings then on the one hand help us to understand the high efficiency of brain networks, on the other hand, guide the design of communication networks and power grids. From the dynamical perspective, as another example, the firing pattern of neural networks is associated with the underlying connection networks, which are considered to be essential to understand the observed brain dynamics. The topological and dynamical perspectives are complementary, and joint investigation of these two is the key to deepening our understanding of the emergence of collective phenomena in various disciplines.
Despite the devoted efforts and fruitful achievements in the last decades, there are gaps that still need to be filled. In the fundamental theory, novel indexes are needed to better characterize different aspects of local and global features of networks; the association of network structure with collective system behaviors is not yet established; the impact of reorganization of network topology on resulting collective dynamics, e.g. synchronization and anomalous oscillations, has to be better quantified. Regarding the multi-disciplinary applications, we still lack methods to well and truly infer the complex underlying networks from complex observed data; there still exist difficulties in predicting outcoming behaviors of a complex system from its network topology and units dynamics; further, how to understand the working mechanisms of natural complex systems and how to design manmade complex systems according to modern network theory are still open questions.
This Research Topic aims to extend the research horizon, and collect articles with a focus on the fundamental theory of complex networks to multi-disciplinary applications of network theory. The editors of this Research Topic will guarantee a fast, but fair, peer-to-peer review procedure, to provide society with a reliable injection of scientific insights. High-quality Original Research and Review articles in this field are all welcome for submission to this Research Topic. Research interests include but are not limited to the following areas:
• Modeling of multiplex networks;
• Dynamical systems on networks;
• Network construction from data;
• Machine learning and big data analysis based on networks;
• Novel method to quantify network topology;
• Application of network theory to social systems, brain networks, power grids, and so on.
