GEFRFE extends generalized frequency filtering embedding into graph fractional Fourier domains via fractional Laplacian eigenvectors, nonlinear composition, and adaptive fractional order selection to improve graph classification on benchmarks.
A matrix representation of graphs and its spectrum as a graph invariant
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abstract
We use the line digraph construction to associate an orthogonal matrix with each graph. From this orthogonal matrix, we derive two further matrices. The spectrum of each of these three matrices is considered as a graph invariant. For the first two cases, we compute the spectrum explicitly and show that it is determined by the spectrum of the adjacency matrix of the original graph. We then show by computation that the isomorphism classes of many known families of strongly regular graphs (up to 64 vertices) are characterized by the spectrum of this matrix. We conjecture that this is always the case for strongly regular graphs and we show that the conjecture is not valid for general graphs. We verify that the smallest regular graphs which are not distinguished with our method are on 14 vertices.
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cs.LG 1years
2025 1verdicts
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Graph Embedding in the Graph Fractional Fourier Transform Domain
GEFRFE extends generalized frequency filtering embedding into graph fractional Fourier domains via fractional Laplacian eigenvectors, nonlinear composition, and adaptive fractional order selection to improve graph classification on benchmarks.