Network Science in Our World
One application of network science is the four-color theorem in graph theory, which shows, for example, that it is possible to color a map of the United States with no two states of the same color touching, using only four colors.
In a famous 1967 study, Stanley Milgram showed that our society is more closely connected than we realize. For almost any two people in the USA, there is a short “friend-of-a-friend-of-a-friend-of…” path connecting them, a phenomenon often called “six degrees of separation.” Sociologists have also defined a number of social network metrics to identify, for example, individuals who are gatekeepers, or social groups that are insular.
More recently, computer scientists have developed efficient algorithms for finding shortest paths in networks, identifying communities of interacting individuals, computing social network metrics, and so on. Google was founded based on the ability of the PageRank algorithm to rank a network of web pages according to their relevance to a user’s search query. The science of networks brings together perspectives from many different disciplines, all of which rely on the powerful network algorithms provided by computer science.
Network science is the general study of the connections that arise in many different contexts. However, some usages of the word “network” belong to different research areas. Bayesian networks are kind of probabilistic graphical model: see Advanced Data Exploitation. Neural networks are machine learning technique: see Machine Learning / AI.
Metron leverages methods from all areas of network science, with a particular emphasis on network inference.