Complex Networks: Generate Simple to Chaotic Real World Networks
Complex networks are absolutely everywhere, that’s what makes the field of network science so interdisciplinary. The relatively new discipline was formalised around 20 years ago as a domain in its own right. Its applications span areas including sociology, communications, biology, physics and computer science to name a few.
In this post i’ll give you an introduction complex networks, run through some basic ways of describing complex networks and attempt to model a real life complex network in Python, strap in.
“A complex network is a graph (network) with non-trivial topological features”… “(Features that) often occur in graphs modelling of real systems”
Complex networks are nothing more than graphs on steroids. What makes complex networks complex is the fact that its subgraphs have varying degrees of randomness and uniformity baked in. This reflects the chaotic myriad of interactions we see arise through natural processes in the real world. You might be thinking, “hang on a minute, but these networks might as well be random?”. They may seem that way but there are universal patterns can we find. We can also apply graph analysis on them to get a clearer understanding and we can generate artificial versions of them with Python. So let’s do that.
Understanding and modelling complex networks lets you do cool, useful and ground breaking things. Here are some examples of questions you can answer:
