BarabásiAlbertnätverk
Barabási-Albert networks, often abbreviated as BA networks, are a type of generative model for random networks. They are characterized by two key mechanisms: growth and preferential attachment. Growth means that the network starts with a small number of nodes and new nodes are added over time. Preferential attachment means that as new nodes are added, they are more likely to connect to existing nodes that already have a high degree (i.e., many connections). This mechanism is often described as "the rich get richer."
The Barabási-Albert model was introduced by Albert-László Barabási and Réka Albert in 1999. It was developed
The BA model is able to reproduce this scale-free property. The growing nature of the network allows