Network Science

About the Book

Network science is the study of connected things. Things can be represented by people, devices, companies, governments, agencies, bank accounts, etc. Connected can be represented by calls, messages, likes, money transferring, references, geo‐positioning, contracts, etc.

The study of networks has emerged in diverse disciplines as a means of analyzing complex relational data. Every industry, in every domain, has information that can be analyzed in terms of linked data. Network science can be applied to understand spatiotemporal events like virus spread or traditional business events like churn and product adoption in telecommunications and entertainment, or service consumption in retail, fraud in insurance, money laundering in banking, among many other business cases.

Network analysis includes graph theory algorithms that can augment statistical and machine learning modeling. In many practical applications, pairwise interaction between the entities of interest in the model often plays an important role. This role can be used as input or independent variable in supervised models and often they turn out to be one of the best predictors. Network analysis goes beyond traditional unsupervised modeling like clustering and supervised modeling like predictive models. Both supervised and unsupervised models are frequently used to identify hidden patterns in data. However, these models are based on the attributes describing the observations, normally entities. Network science ...