Well-known examples include semantic networks (in which concepts are connected through shared meanings 5), social networks (in which people are connected through acquaintance 6) and neural networks (in which neurons are connected through axons 7). Network approaches involve the identification of system components (network nodes) and the relations among them (links between nodes).
This Primer discusses methodology to apply this line of reasoning to the statistical analysis of multivariate data. The value of analysing the structure of a system in this way has been underscored by the advent of network science, which has delivered important insights into diverse sets of phenomena studied across the sciences 3, 4. Instead, one must also study the organization of the system’s components, which can be represented in a network 2. To understand such phenomena, it is often insufficient to focus on the way individual components of a system operate. In many scientific fields, researchers study phenomena best characterized at the systems level 1.
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