To understand algorithms related to complex systems, writing, executing and optimising simulation programs of such systems (Montecarlo and molecular dynamics programs) and analysing the data produced by simulations.
Curriculum
teacher profile teaching materials
- Graphs, trees and networks
- Centrality measures and degree
- Random graphs, the Erdős and Rényi model
SMALL WORLDS NETWORKS
- Definition of Small World
- Clustering Coefficient
- The Watts-Strogatz model
GENERALISED RANDOM GRAPHS
- Statistical description of networks
- Degree Distributions of real networks
- Generalization of the Erdős–Rényi model
- Radom graphs with power-law degree distributions
GROWING GRAPHS
- Dynamical evolution of random graphs
- The Barabási–Albert model
DEGREE CORRELATIONS
- Correlated networks
- Assortative and Disassortative Networks, "Rich Club" behavior
WEIGHTED NETWORKS
- Beyond purely topological networks: tuning the interactions in a complex system
- The Barrat-Barthélemy-Vespignani model
INTRODUCTION TO DYNAMICAL PROCESSES: THEORY AND SIMULATION
V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Cambridge University press (2017)
The course also follows selected parts of the book:
A. Barrat, M. Barthelemy, A. Vespignani, "Dynamical processes on complex networks", Cambridge University Press (2008)
Programme
NETWORKS AND GRAPHS- Graphs, trees and networks
- Centrality measures and degree
- Random graphs, the Erdős and Rényi model
SMALL WORLDS NETWORKS
- Definition of Small World
- Clustering Coefficient
- The Watts-Strogatz model
GENERALISED RANDOM GRAPHS
- Statistical description of networks
- Degree Distributions of real networks
- Generalization of the Erdős–Rényi model
- Radom graphs with power-law degree distributions
GROWING GRAPHS
- Dynamical evolution of random graphs
- The Barabási–Albert model
DEGREE CORRELATIONS
- Correlated networks
- Assortative and Disassortative Networks, "Rich Club" behavior
WEIGHTED NETWORKS
- Beyond purely topological networks: tuning the interactions in a complex system
- The Barrat-Barthélemy-Vespignani model
INTRODUCTION TO DYNAMICAL PROCESSES: THEORY AND SIMULATION
Core Documentation
Main text-book:V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Cambridge University press (2017)
The course also follows selected parts of the book:
A. Barrat, M. Barthelemy, A. Vespignani, "Dynamical processes on complex networks", Cambridge University Press (2008)
Type of delivery of the course
The education consists of lectures and programming sessions.Attendance
It is recommended to attend all lectures.Type of evaluation
The knowledge of the students will be examined through written and oral project presentations and questions concerning the theory of the course. teacher profile teaching materials
- Graphs, trees and networks
- Centrality measures and degree
- Random graphs, the Erdős and Rényi model
SMALL WORLDS NETWORKS
- Definition of Small World
- Clustering Coefficient
- The Watts-Strogatz model
GENERALISED RANDOM GRAPHS
- Statistical description of networks
- Degree Distributions of real networks
- Generalization of the Erdős–Rényi model
- Radom graphs with power-law degree distributions
GROWING GRAPHS
- Dynamical evolution of random graphs
- The Barabási–Albert model
DEGREE CORRELATIONS
- Correlated networks
- Assortative and Disassortative Networks, "Rich Club" behavior
WEIGHTED NETWORKS
- Beyond purely topological networks: tuning the interactions in a complex system
- The Barrat-Barthélemy-Vespignani model
INTRODUCTION TO DYNAMICAL PROCESSES: THEORY AND SIMULATION
V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Cambridge University press (2017)
The course also follows selected parts of the book:
A. Barrat, M. Barthelemy, A. Vespignani, "Dynamical processes on complex networks", Cambridge University Press (2008)
Programme
NETWORKS AND GRAPHS- Graphs, trees and networks
- Centrality measures and degree
- Random graphs, the Erdős and Rényi model
SMALL WORLDS NETWORKS
- Definition of Small World
- Clustering Coefficient
- The Watts-Strogatz model
GENERALISED RANDOM GRAPHS
- Statistical description of networks
- Degree Distributions of real networks
- Generalization of the Erdős–Rényi model
- Radom graphs with power-law degree distributions
GROWING GRAPHS
- Dynamical evolution of random graphs
- The Barabási–Albert model
DEGREE CORRELATIONS
- Correlated networks
- Assortative and Disassortative Networks, "Rich Club" behavior
WEIGHTED NETWORKS
- Beyond purely topological networks: tuning the interactions in a complex system
- The Barrat-Barthélemy-Vespignani model
INTRODUCTION TO DYNAMICAL PROCESSES: THEORY AND SIMULATION
Core Documentation
Main text-book:V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Cambridge University press (2017)
The course also follows selected parts of the book:
A. Barrat, M. Barthelemy, A. Vespignani, "Dynamical processes on complex networks", Cambridge University Press (2008)
Type of delivery of the course
The education consists of lectures and programming sessions.Attendance
It is recommended to attend all lectures.Type of evaluation
The knowledge of the students will be examined through written and oral project presentations and questions concerning the theory of the course.