Month: February 2014

Finding Community

sdenton4

I attended a really nice talk by Arash Amini yesterday about detecting ‘communities’ in sparse graphs.  The basic problem is: In a big graph (like the Facebook graph, or the graph of scientific papers citations) you have clusters of friends/associates, and you want to pick out those clusters. Dr. Amini and his collaborators have been working on methods to solve this problem in particularly noisy cases.  The methods presented were developed for cases where you know the specific number of clusters you are seeking to find.  It was a fascinating talk, and I came away wondering if there are good methods to use when you aren’t sure of how many clusters there are.

communities
At the left is a randomly generated block-diagonal adjacency matrix (using a stochiastic block model). The center is the same matrix with the rows randomly shuffled; you can’t “see” that it has block diagonal structure. The right is the matrix with blocks recovered using the techniques outlined in this post. Note the noise in the lower-right corner; these correspond to individual, isolated vertices that ended up in their own clusters.

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RoShamBo Part IV: Implementation

sdenton4

I’ve finally had a bit of time to finish up the code for the Rock-Paper-Scissors bot discussed in the previous posts; I’ve put the code in a GitHub repository here.  Feel free to file issues if you would like it to be a bit more user friendly.

Win percentage over time, Diaconis vs Switchbot. As time increases, the win percentage settles in on 2/3's which is a natural limit for play against Switchbot.
Win percentage over time, Diaconis vs Switchbot. As time increases, the win percentage settles in on 2/3’s which is a natural limit for play against Switchbot.

The bot which uses the Fourier transform on move probabilities to search for profitable patterns is named `diaconis`, after Persi Diaconis. It’s currently working just fine, but is a bit slow computationally. On startup, it generates all possible move patterns that it will investigate during the course of each game, as well as some character tables. After each play, it tests some of these patterns, and tries to determine if their predictive power is better than any of the patterns seen thus far. If so, it begins using that pattern to choose move probabilities.

This works fine as a proof-of-concept of the basic ideas. Additional improvements could be had by doing some code optimization to speed things up a bit, and keeping a list of good patterns and allowing a bit more dexterity in switching between the patterns used for prediction.