Network Science

1Concepts in Network Science

1.1 Introduction

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 different types of spatiotemporal events, from (virus spread to traditional business events such as churn and product adoption in telecommunications and entertainment, service and product consumption in retail industry, fraud or exaggeration in insurance, or money laundering and fraudulent transactions in banking, among many others business scenarios.

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 and can be used as input or independent variables in supervised models. Very 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 ...