TY - JOUR
T1 - From the Difference of Structures to the Structure of the Difference
AU - Zanin, Massimiliano
AU - Menasalvas, Ernestina
AU - Sun, Xiaoqian
AU - Wandelt, Sebastian
N1 - This paper is supported by the National Natural Science Foundation of China (Grants no. 61650110516 and No. 61601013).
PY - 2018/1/1
Y1 - 2018/1/1
N2 - When dealing with evolving or multidimensional complex systems, network theory provides us with elegant ways of describing their constituting components, through, respectively, time-varying and multilayer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a general framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. Differently from other approaches, which focus on assessing the magnitude of the change, the proposed one allows understanding if the observed changes are due to random noise or to structural (targeted) modifications; in other words, it allows describing the nature of the force driving the changes and discriminating between stochastic fluctuations and intentional modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social, and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow converting the system's evolution to the frequency domain.
AB - When dealing with evolving or multidimensional complex systems, network theory provides us with elegant ways of describing their constituting components, through, respectively, time-varying and multilayer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a general framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. Differently from other approaches, which focus on assessing the magnitude of the change, the proposed one allows understanding if the observed changes are due to random noise or to structural (targeted) modifications; in other words, it allows describing the nature of the force driving the changes and discriminating between stochastic fluctuations and intentional modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social, and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow converting the system's evolution to the frequency domain.
UR - http://www.scopus.com/inward/record.url?scp=85059511264&partnerID=8YFLogxK
U2 - 10.1155/2018/4326097
DO - 10.1155/2018/4326097
M3 - Article
AN - SCOPUS:85059511264
VL - 2018
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 4326097
ER -