One Weird Fourier Trick for Combinatorial Data

 

Cell phone photo from the scene of the crime
Cell phone photo from the scene of the crime

Here are the slides from the talk I gave in Montreal last Friday.

The talk was about using Fourier transforms to get polynomial-time encodings of permutation statistics, as well as a look at the Kondor-Borgwardt approach to graph invariants via the Fourier transform over S_n.  The talk was given at a representation theory conference, and I was making the point that we can get new research ideas by taking trips into the world of applications – in my case, by looking at machine learning problems.  The opening joke was that I asked my computer for the best possible title for the talk, and received the click-bait title as a response.  It was admittedly a pretty funny moment watching the chair of the session trying to decide whether to read the title originally submitted for the talk (‘Compressed Combinatorial Statistics’) or the ridiculousness on the screen (he went with the original).

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Finding Community

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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