Todd Hildebrant

Todd Hildebrant

We study independent sets in strong powers of circulant graphs using a transfer matrix formulation. The compatibility constraints separate into intra-layer and inter-layer components, yielding a transfer operator that is equivariant under the dihedral group action. The characteristic polynomial of the transfer operator factors into an \emph{anomalous} component (arising from the trivial isotypic component, with rational coefficients) and a \emph{cyclotomic} component (arising from nontrivial Fourier modes, splitting over the maximal real cyclotomic subfield). We show that the spectral radius is attained in the trivial isotypic component, so the dominant exponential growth is governed by a low-dimensional orbit-compressed operator. The independence polynomial is computed exactly for strong cylinders and tori, with the cyclotomic sector contributing a sparse correction confined to high-weight coefficients. All results are verified for $C_7$.

Todd Hildebrant

This paper explores the application diffusion maps as graph shift operators in understanding the underlying geometry of graph signals. The study evaluates the improvements in graph learning when using diffusion map generated filters to the Markov Variation minimization problem. The paper showcases the effectiveness of this approach through examples involving synthetically generated and real-world temperature sensor data. These examples also compare the diffusion map graph signal model with other commonly used graph signal operators. The results provide new approaches for the analysis and understanding of complex, non-Euclidean data structures.