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Efficient eigenvalue counts for tree-like networks

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Autor(es):
Guzman, Grover E. C. ; Stadler, Peter F. ; Fujita, Andre
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF COMPLEX NETWORKS; v. 10, n. 5, p. 15-pg., 2022-08-23.
Resumo

Estimating the number of eigenvalues mu([a,b]) of a network's adjacency matrix in a given interval [a,b] is essential in several fields. The straightforward approach consists of calculating all the eigenvalues in O(n(3)) (where n is the number of nodes in the network) and then counting the ones that belong to the interval [a,b]. Another approach is to use Sylvester's law of inertia, which also requires O(n(3)). Although both methods provide the exact number of eigenvalues in [a,b], their application for large networks is computationally infeasible. Sometimes, an approximation of mu([a,b]) is enough. In this case, Chebyshev's method approximates mu([a,b]) in O(vertical bar E vertical bar) (where vertical bar E vertical bar is the number of edges). This study presents two alternatives to compute mu([a,b]) for locally tree-like networks: edge- and degree-based algorithms. The former presented a better accuracy than Chebyshev's method. It runs in O(d vertical bar E vertical bar), where d is the number of iterations. The latter presented slightly lower accuracy but ran linearly (O(n)). (AU)

Processo FAPESP: 20/08343-8 - Análise (espectral) de grafos/hipergrafos para comparar redes metabólicas do patógeno Trypanosoma sp.
Beneficiário:André Fujita
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 19/22845-9 - Abordagens computacionais com o objetivo de explorar interações intra e inter espécies e seu papel em todos os domínios da vida
Beneficiário:André Fujita
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/21934-5 - Estatística de redes: teoria, métodos e aplicações
Beneficiário:André Fujita
Modalidade de apoio: Auxílio à Pesquisa - Temático