  We outline a novel clustering scheme for simplicial complexes that produces
clusters of simplices in a way that is sensitive to the homology of the
complex. The method is inspired by, and can be seen as a higher-dimensional
version of, graph spectral clustering. The algorithm involves only sparse
eigenproblems, and is therefore computationally efficient. We believe that it
has broad application as a way to extract features from simplicial complexes
that often arise in topological data analysis.
