Random Walks on Graphs: Multiscale Aspects

Mauro Maggioni
Duke University
Mathematics and Computer Science

In the first set of tutorials, we looked at the behavior of random walks on graphs for large time. Here we focus on multiscale analysis of and on graphs: it is possible to look at a random walk at all possible time scales in a coherent multiscale fashion, that corresponds to creating wavelets on graphs. We discuss the geometric aspects, which correspond to a geometric compression of graphs, as well as the analysis aspects, corresponding to a wavelet analysis of functions on graphs. We discuss applications to machine learning and organization of large data sets.

Presentation (PDF File)

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