On Eigenvalues of Random Complexes
Anna Gundert, Uli Wagner

TL;DR
This paper investigates the eigenvalues of higher-dimensional Laplacians and adjacency matrices in random simplicial complexes, revealing concentration phenomena and limitations of higher-dimensional Cheeger inequalities.
Contribution
It develops an analogous eigenvalue relation for adjacency matrices in random complexes and demonstrates the failure of a straightforward higher-dimensional Cheeger inequality.
Findings
Eigenvalues concentrate around two values for p=Ω(log n/n)
Higher-dimensional Cheeger inequality does not hold universally
Constructs complexes with strong spectral expansion but weak coboundary expansion
Abstract
We consider higher-dimensional generalizations of the normalized Laplacian and the adjacency matrix of graphs and study their eigenvalues for the Linial-Meshulam model of random -dimensional simplicial complexes on vertices. We show that for , the eigenvalues of these matrices are a.a.s. concentrated around two values. The main tool, which goes back to the work of Garland, are arguments that relate the eigenvalues of these matrices to those of graphs that arise as links of -dimensional faces. Garland's result concerns the Laplacian; we develop an analogous result for the adjacency matrix. The same arguments apply to other models of random complexes which allow for dependencies between the choices of -dimensional simplices. In the second part of the paper, we apply this to the question of possible higher-dimensional analogues of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
