Excerpt

The field of network science as we know it today is widely thought to have been ignited by Duncan Watts and Steve Strogatz’s 1998 paper “The Collective Dynamics of ‘Small-World’ Networks.” Network science is a highly interdisciplinary field that focuses on how the pattern of connectivity between a system’s components is critical to its function. Applications range from brain networks to power grids to epidemics. Researchers working in network science have a wide diversity of backgrounds, including physical, mathematical, social, biological, and computational sciences. In trying to explain complex phenomena by probing interaction patterns, network science is also closely tied to complex-systems science.

While network science as a field is relatively young, researchers were, of course, studying networks long before Watts and Strogatz introduced their “small-world” network model. The difference between research within the sphere of what we now call network science and those earlier studies (e.g., graph theory proofs by mathematicians, network algorithms by computer scientists, social network analysis by sociologists) is that the aim of network science, like that of complex-systems science, is to unravel and understand the common drivers of complexity that appear across a wide range of applications.

Bibliography

Barabási, A.-L., and R. Albert. 1999. “Emergence of Scaling in Random Networks.” Science 286 (5439): 509–512. https://doi.org/10.1126/science.286.5439.5.

Newman, M. 2018. Networks: An Introduction. Oxford, UK: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001.

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