## Preprint on modeling paths with graph neural networks

Jean-Baptiste Cordonnier and I released a new preprint focusing on the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we aim to predict which nodes are in the missing suffix.

Extrapolating paths with graph neural networks

For more information, see *this blog-post*.

## Review article on sampling techniques

Nicolas Tremblay and I recently dived into the very interesting literature of sampling methods for spectral clustering. Here is the (pre-print) of our labors:

Approximating Spectral Clustering via Sampling: a Review (under consideration as a chapter of the book “*Sampling methods for machine learning*“)

In it, we discuss a lot of cool techniques from numerical linear algebra and machine learning (such as *Nystrom approximation*, *Random Fourier Features*, *Coresets*, *Landmarks, Sparsification*), in addition to summarizing our recent discoveries with *random graph filters* and *coarsening*.

## New work on graph coarsening

I have been thinking for some time now on *how can we reduce the size of a graph without significantly altering its basic properties*. In my latest work, I present coarsening algorithms that preserve the *spectrum of a graph* as well as its *community structure*.

Graph reduction with spectral and cut guarantees

A demonstration can be found in *this blog-post*.