You can make networks from pretty much anything. Connect people based on friendships or phone calls, proteins based on interaction, words based on sound or meaning, and lots more.

It is high time to start using networks to understand games. And this is already beginning to happen. Aaron Clauset and Winter Mason have used a massive dataset from the videogame Halo to understand teams and how they operate.

____But what of the structure of games themselves? In a paper that was recently published in Europhysics Letters, two French scientists decided to apply network science to the game of Go.

They constructed their networks in a simple way: If one board position can lead to another, they are connected. Using a dataset of about 1,000 professional games and 4,000 amateur games, they began to construct these networks.

Of course, the Go board is very large and so you can’t compare entire board layouts. Instead, they decided to make it much more tractable and look at the board composition surrounding a newly placed piece (a move in Go consists of putting a stone on an intersection of the grid lines of the board). In this case, they looked at the pieces immediately surrounding a newly placed piece (for a 3x3 grid). They calculated that this creates 1107 possible moves, which can be connected if the moves occur one after another, and are in the same region of the board. They also examined the frequency of moves, which obeys a heavy-tailed distribution (whether or not it is a power-law as they claim seems a bit weaker).

The network analyses in the paper are a bit odd, though they find many classic graph structures, such as a heavy-tailed link distribution and high amounts of clustering. Gratifyingly, the networks constructed from amateur and professional games are distinct, though in somewhat subtle ways. And while I know that network pictures are usually inscrutable hairballs, I was disappointed that that networks weren’t visualized at all.

Nevertheless, this a fun little network analysis and I recommend checking it out.

Next up, a network analysis of KerPlunk?

Top image: Reilly Butler/Flickr/CC-licensed