Page 55 - Complexity Perspective_Neat

Researchers in many fields have abstracted the idea of an interaction
such that it may represent anything that has a hypothesized
relationship with anything else. It has been used to study such diverse
subjects as patterns of academic citations, the common use of
ingredients in recipes, as well as the spread of a bacterium linked to a
disease. Almost anything where a correlation exists or can be
hypothesized.

These relationships can be analyzed using statistical techniques such
as cluster analysis that group like nodes or like links with like. They
can also be illustrated using a mathematical technique called graphical
analysis, which is simply a vehicle for displaying static and dynamic
relationships pictorially that would otherwise be buried in millions of
data points in a database. Software packages now render graphical
analysis easy.

Network analysis finds its biggest use in business facilitating
segmentation analysis; here the attributes of nodes and links are
examined to reveal commonalities and buying behaviors. Examined
over time, dynamic models of actual changes in behavior can be
mapped or predicted by varying parameters. These same tools are
used to model the spread of diseases (epidemiology), predict virus
spreading in computer networks, and anticipate cascading failures in
electrical grids and much more.

The Social Media Platforms

Awareness of networks has boomed in the last two decades in the
general population with the arrival of social media platforms on the
internet.

The big social media platforms have facilitated mass communication
coupled with instant data gathering on people and links across the
internet. This treasure trove of dynamic data has revolutionized
marketing.

Facebook, Twitter, Google, LinkedIn, eBay, TripAdvisor, etc. collect
data on the nodes (you and me, with our attributes: age, sex, location,
attitudes, etc. etc.) and our links/ties (to whom, their strength,
direction, degree). These data are sold commercially for purposes

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