At its most basic, a network can be defined as a collection of ‘nodes’ (things like people, products, or web pages) connected by relationships or associations of some kind (known as ‘edges’).
The fundamental insight of network analysis is that a node’s position in the network reflects its importance.
For example, a website at the centre of a network of links, and a person at the centre of a community of people, will both have more importance or influence than others at the fringes.
Modelling a real-world phenomenon as a network therefore gives us at least one insight we otherwise wouldn’t have had.
Network analysis can reveal much more than this, however. For example, using network analysis we can:
- identify communites, or other groups of similar nodes (eg similar websites)
- understand how information, behaviours or even diseases flow through a network of contacts
- track network change over time
- overlay the network with other metrics and understand how measures such as network centrality correlate with, for example, wealth or opportunities for promotion within a company
- visualise the network to get a bird’s eye view.
The insights of network analysis are surprisingly consistent across networks of various types – whether online or offline, and whether the nodes are people, websites, concepts or even words.
Once you start looking for networks and network effects, you see them everywhere.