CS计算机代考程序代写 SWEN90004

SWEN90004
Modelling Complex Software Systems
Lecture Cx.10 Networks II: Networks and ABMs
Artem Polyvyanyy, Nic Geard
artem.polyvyanyy@unimelb.edu.au;
nicholas.geard@unimelb.edu.au
Semester 1, 2021
1/9

Recap
􏰀 Networks can be used to represent the structure of complex systems – the pattern of interactions between their components
􏰀 We can describe the structure of networks in terms of quantitive properties such as density, degree distribution, characteristic path length, clustering coefficient, etc.
􏰀 A range of network models have been proposed to describe the structure of real world complex systems, including:
􏰀 small world networks 􏰀 scale free networks
􏰀 We discussed how regular 1D and 2D lattices could be used to describe the structure of interactions in 1D and 2D cellular automata.
􏰀 Today we will switch focus from network structure to network dynamics.
2/9

Network dynamics
Two types of network dynamic are often considered:
􏰀 the network structure may remain static, but the state of nodes changes—dynamics on networks
􏰀 the network structure changes—dynamics of networks
3/9

Dynamics on networks—SIR disease model
In week 3, we explored a Cellular Automata model of disease trans- mission on a 2D grid. As a grid can be represented as a regular lattice, it is straightforward to generalise our approach to consider the spread of infection across any network.
4/9

Dynamics on networks—SIR disease model
􏰀 what effect might network structure have on the dynamics of disease spread?
􏰀 which nodes in a network are at greatest risk: 􏰀 of being infected?
􏰀 of infecting others?
􏰀 what does this suggest about approaches to preventing the
spread of infection?
5/9

Dynamics on networks—SIR disease model
The spread of Severe Acute Respiratory Syndrome (SARS) in 2002–2003.
Netlogo model: Virus on a Network
6/9

Dynamics of networks
The growth model we used for creating scale-free networks and the rewiring model we used for creating small world networks are simple examples of network dynamics
In these models, the state of a node can be unimportant.
More complex dynamic behaviour is possible if both network state
and network structure are changing at the same time.
Consider our SIR disease model on a network:
􏰀 under what conditions might we expect edges to be rewired? 􏰀 what effect might this have?
7/9

Adaptive networks
For example, consider a road network:
􏰀 nodes are locations that people travel between
􏰀 edges are roads between locations that people travel on
􏰀 roads have a capacity: if two many people travel along a
certain road, it may become congested (a property of the
network’s state)
􏰀 in response to this congestion, new roads may be built (a
change to the network’s structure)
􏰀 this may, in turn, cause the patterns of travel to change
8/9

Networks and ABMs
What is the relationship between agent-based models (or multi-agent systems) and networks?
In the simplest case, agents are mapped to the nodes of a network, and the network topology determines patterns of interaction between agents.
Network (as opposed to spatial) models are useful when:
􏰀 the relationships among agents are more important than their
physical locations; eg, a model of information spreading through a population (potentially via phone or email), versus a model of a fire spreading through a forest
􏰀 interactions between agents are not grounded in physical
proximity; eg, a model of interacting software agents, versus a
predator-prey model.
What are other complex systems in which network structure may be more appropriate than spatial structure?
9/9