Summary on networks
Topic: Networks
by Liyao, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
Networks, or networks theory, is a powerful tool to unravel complexity in a complex system. The networks, or usually called graph, is an abstract representation of a complex system with objects in the system abstracted into nodes and their relations between each other into edges between nodes. There are mainly two mainly two phases in applying network theory, which is firstly abstracting system into networks and secondly analysing the networks from different perspectives.
Sometimes it is natural to analyse some complex systems as networks, social networks, molecular networks and other biological networks such as neurones networks and such as biologic chain discussed in previous speech. Typically, with the growth of computability, social networks analysis is widely applied in the study of diffusion of news, structure of markets and other activities in the society.
Networks theory is also applicable when people modelling evolving events. For example, people may model the recruitment of political movements and even uncover the development of insurgent networks in military.
Some other times it may require some more talents to abstract people and parties into nodes and involved events as edges to analyse key actors in a big movement. In this way, we can create a narrative and the example network for US Elections 2012 is shown as below.
Besides, Google abstracts the internet into a graph with web pages as nodes and web links as edges and then builds their fundamental algorithm for web search based on such graph.
After abstracting real world scenario into networks, people can analyse those abstract networks from different perspectives.
Some usual metrics include total links length, which is the sum of all edges in the networks, robustness, which is how easy to break a network into two separate networks after removal of some nodes or edges, and transport efficiency in terms of the average length from any given nodes to travel to any other nodes. For example, researchers abstract road networks and foraging network created by bacteria colony into graphs, then analyse the graphs with total length, robustness, and transport efficiency as metrics. Finally, they proposed a bio-inspired networks model to guide the construction of roads, which can achieve an optimum in terms of those metrics.2
In addition to analysis within one complex system, people may also want to analyse the dependence between systems, which corresponds to the analysis interdependence and independence between networks. Some researchers abstracted our neurons networks in the brain into graphs and analyse the dependency between groups of nodes, which are neuron cells before abstraction, by observing how excitatory passes through the networks and activates other nodes. After grouping cells into subnets, they came up with new insight into how humans creativity works. They claim that human memorises different patterns of things in different independent neurones sub-networks and then creates new combinations using those memorised patterns, thus conceiving things never seen before.3 There are more research going on regarding both how to abstract things into networks and how to analyse the attributes of networks. An example could be how to model the memory in human brain as a network and on which properties of this network we shall focus? 2 https://pdfs.semanticscholar.org/3881/fa370a0a434d98936455e999e328cf297ef3.pdf 3 Bar-Yam, Making Things Work, Chapter 3 on Networks and Collective Memory
Want more?#
Here is a great YouTube video to introduce you into the network theory if you want to go into it further. https://www.youtube.com/watch?v=qFcuovfgPTc
Disclaimer#
This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au