collective dynamics of ‘small world’ networks
DESCRIPTION
Collective Dynamics of ‘Small World’ Networks. C+ Elegans: Ilhan Savut, Spencer Telford, Melody Lim. 29/10/13. Networks of dynamical systems. Networks of dynamical systems. Can be represented by points (vertices or nodes) connected with lines (edges). Other Networks. - PowerPoint PPT PresentationTRANSCRIPT
Collective Dynamics of ‘Small World’ Networks
C+ Elegans: Ilhan Savut, Spencer Telford, Melody Lim
29/10/13
Networks of dynamical systems
Networks of dynamical systems
• Can be represented by points (vertices or nodes) connected with lines (edges)
Other Networks
Overview
• Different types of networkso ‘Collective Dynamics of Small World Networks’ Duncan Watts and
Steven Strogatz, Nature 393:440-442 (1998)
• Properties of networkso New type of network!
• Applications
A Regular Network
A Random Network
Fine-Tuning p Changes Network Properties• p - rewiring of regular network
• Properties depend heavily on p
Properties of Networks
• Path Length, L
• Clustering coefficient, C
Properties of Networks
• Path Length, Lo “Degrees of separation”
• Clustering coefficient, Co Chances that your friends are friends with each
other
Path Length for Networks
Long Short
Clustering for Networks
High Low
An Example of a Regular Network
No Random Networks Exist in Nature
L and C Depend on p
New Type of Network• 0 < p < 1 (not fully random, not fully regular)
Small-World Networks
• Short path length, highly clustered
‘Small-world’ Network in the Middle
Small World Networks Are Natural
• Formation of networks favors small world • Most networks built from small elements• Evolutionary and natural processes favor the
formation of small-world networks
Many Real World Systems are Small World Networks
• Collaboration graph of actors (six degrees of separation study)
• Neural network of C. elegans• Power grid of Western US
Small World Networks Have New PropertiesRobustness• Resistant to random changes• Targeted ‘attacks’
Small World Networks Model the Spread of Disease
ConclusionNew class of network model• Low path length• High clustering coefficient• Models many natural systems!