Lancichinetti–Fortunato–Radicchi benchmark
So imagine you're playing a game with your friends where you draw a picture of a family tree. But instead of just drawing any family tree, you have to follow some rules.
Now, imagine you have a lot of friends playing this game with you, and you all want to compare your family trees to see who did the best job of following the rules.
The Lancichinetti–Fortunato–Radicchi benchmark is like that game, but for scientists who study networks (like social networks or computer networks). Instead of a family tree, they create a network of points connected by lines. And instead of rules for drawing the network, they use some math to create a set of rules that the network must follow.
The math they use is pretty complicated, but the idea is to create a benchmark (a standard) for how good different algorithms (like computer programs) are at creating networks that follow those rules.
So scientists can create lots of different algorithms for making networks, and then they test them all on this benchmark. The ones that create networks that are the closest to the benchmark are considered the best ones.
It's like if you and your friends all had different ways of drawing family trees, and you tested them all by comparing them to a really good example of a family tree. The one that looks closest to the best example is the best one.
Now, imagine you have a lot of friends playing this game with you, and you all want to compare your family trees to see who did the best job of following the rules.
The Lancichinetti–Fortunato–Radicchi benchmark is like that game, but for scientists who study networks (like social networks or computer networks). Instead of a family tree, they create a network of points connected by lines. And instead of rules for drawing the network, they use some math to create a set of rules that the network must follow.
The math they use is pretty complicated, but the idea is to create a benchmark (a standard) for how good different algorithms (like computer programs) are at creating networks that follow those rules.
So scientists can create lots of different algorithms for making networks, and then they test them all on this benchmark. The ones that create networks that are the closest to the benchmark are considered the best ones.
It's like if you and your friends all had different ways of drawing family trees, and you tested them all by comparing them to a really good example of a family tree. The one that looks closest to the best example is the best one.