Written for the course `Random Graphs' at Eindhoven University of Technology, and class room tested for over ten years, the book `Random Graphs and Complex Networks' has now finally appeared in print at Cambridge University Press. According to the cover, this is `the definitive introduction to the mathematical world of random networks' and `a modern and deep, yet accessible, introduction to the models that make up that basis for the theoretical study of random graphs and complex networks'.
Has this whet your appetite? Click here for more information about the book at its Cambridge University website.
Courtesy of Cambridge University Press, we can offer a discount to all contributors to the Network Pages. Interested? Send us a mail! More details will follow!
One of the main building blocks of modern AI-tools are artificial neural networks, abstract models inspired by the structure and functions of biological neural networks which enable machines to "learn". In this article, I will discuss some thoughts on this topic.
The scientific world would not be what it is today without normal distribution. It is the foundation of many statistical models for several good reasons. Most importantly, it appears commonly in nature. But this is only the tip of the iceberg.
If, after reading the title, your immediate response is to shout "1/6-th", then you have correctly answered the question. Well done! However, in this article we will focus on the meaning of this question. What exactly is this "chance" of which you've just exclaimed it equals 1/6-th?
We want our guts to be filled with a diverse range of mutually beneficial and competitive interactions, a perfect blend of friends, frenemies, and enemies to keep our guts active and body on its toes. Read how networks can help understand these interactions!
