1.1 Intro to networks
1.1.1 Networks and complex systems
A system is a complex system when we cannot know its collective behavior by simply understanding each individual component within it. To really understand complex systems, we need to gain a deeper understanding of networks behind these syetems.
Despite that networks are different in size, nature, function, etc., they are orgnized by a common set of rules. That’s why we are able to study them.
1.1.2 Driving forces behind network science’s emerging as a discipline
Networks are not new. They have been studied by scientists in other disciplines for centuries. But why did network science become a discipline in the 21st centuary? There are two driving forces behind it:
- The emergence of network maps
Mapping interactions in networks requires we keep track of huge amounts of data. This was not readily available until the start of the 21st century.
- The universality of network characteristics
In the past, people knew different networks, but they didn’t realize that as different as these networks are, they are indeed similar and share the same principles.
1.1.3 Key characteristics of network science
Network science has the following key characteristics:
- Interdisciplinary
Scientists in different disciplines working with networks have their own problems to solve and tasks to perform. However, some of their tasks share similarities, and a solution found in discipline A might turn out to be useful and important for discipline B as well.
- Empirical, & data driven
Network science is empirical in nature. Every tool developed by network scientists should pass the test of data in real life and should offer people deeper insights about the network.
- Quantitative and mathematical
Network science has been borrowing the fromalism from graph theory, the conceptual framework from statistical physics, and concepts from engineering.
- Computational
Scientists of network science need to deal with a vast amount of data. They’ll need to apply computational methods.