AN INTRODUCTION TO
#ECONOPHYSICS
Correlations and Complexity in Finance
ROSARIO N. #MANTEGNA
Dipartimento di Energetica ed Applicazioni di Fisica, Palermo University
H. EUGENE #STANLEY
Center for Polymer Studies and Department of Physics, Boston University
http://polymer.bu.edu/hes/book-mantegna00stanley.pdf
#ECONOPHYSICS
Correlations and Complexity in Finance
ROSARIO N. #MANTEGNA
Dipartimento di Energetica ed Applicazioni di Fisica, Palermo University
H. EUGENE #STANLEY
Center for Polymer Studies and Department of Physics, Boston University
http://polymer.bu.edu/hes/book-mantegna00stanley.pdf
Interview with Eugene H. #Stanley
http://www.saha.ac.in/cmp/camcs/Stanley-interview.pdf
#Econophysics
Dr. Eugene H. Stanley (1941–) is one of the most influencing figures in the discipline of #Econophysics. He was born in Oklahoma City, U.S. and was awarded the Ph.D. in physics at Harvard University. In 1976 he joined Boston University as Professor of Physics, and was promoted to Professor of Physiology and University Professor, in 1978 and 1979, respectively. In 2007 he was offered joint appointments with the Chemistry and Biomedical Engineering Departments, and in 2011 he was made William Fairfield Warren Distinguished Professor.
http://www.saha.ac.in/cmp/camcs/Stanley-interview.pdf
#Econophysics
Dr. Eugene H. Stanley (1941–) is one of the most influencing figures in the discipline of #Econophysics. He was born in Oklahoma City, U.S. and was awarded the Ph.D. in physics at Harvard University. In 1976 he joined Boston University as Professor of Physics, and was promoted to Professor of Physiology and University Professor, in 1978 and 1979, respectively. In 2007 he was offered joint appointments with the Chemistry and Biomedical Engineering Departments, and in 2011 he was made William Fairfield Warren Distinguished Professor.
📄 Unification of theoretical approaches for epidemic spreading on complex networks
Wei Wang, Ming Tang, H. Eugene #Stanley, Lidia A. Braunstein
https://arxiv.org/pdf/1612.04216v1
📌 ABSTRACT
Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.
Wei Wang, Ming Tang, H. Eugene #Stanley, Lidia A. Braunstein
https://arxiv.org/pdf/1612.04216v1
📌 ABSTRACT
Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.