Network Science

Preface

I was first exposed to the term network science in 2008 when I moved to Dublin to conduct a post‐doctoral research term at the Dublin City University in partnership with Eircom. By that time, there was much more network analysis, or social network analysis. At first glance, I thought it was related to social media, such as Facebook, Twitter, LinkedIn, and Instagram, along with many others. Many people likely have this misunderstanding. When we mention social network analysis most people are directly pointed to social media, or the analysis of social interactions.

Network science involves several disciplines like social sciences, graph theory, mathematical modeling, statistics analysis, and optimization, to name a few. Assuming that everything is connected, the main goal is to solve complex problems or to understand a scenario in a unique perspective. When I say everything is connected, I mean that most of the real‐world problems can be analyzed in a network perspective, where descriptive attributes are linked to constraints or restrictions, which are linked to possible outcomes or targets, which are linked to goals and solutions, which ultimately are linked to the problems. Even traditional approaches such as predictive modeling can have a network understanding, where input or independent variables are linked to output, target, or dependent variables. How strongly or weakly are they connected to each other? How strongly or weakly are they connected to the target? Surrogate ...