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ETDs @PUC-Rio
Estatística
Título: TWO APPROACHES TO MODERATE DEVIATIONS IN TRIANGLE COUNT IN G(N, M) GRAPHS
Autor: GABRIEL DIAS DO COUTO
Colaborador(es): SIMON RICHARD GRIFFITHS - Orientador
Catalogação: 04/AGO/2022 Língua(s): ENGLISH - UNITED STATES
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=60043&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=60043&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.60043
Resumo:
The study of deviations, and in particular large deviations, has a long history in Probability Theory. In recent decades many articles have considered these questions in the context of subgraphs of the random graphs G(n, p) and G(n, m). This dissertation considers the lower tail for the number of triangles in the random graph G(n, m). Two approaches are considered: Martingales, based on the article of Christina Goldschmidt, Simon Griffiths and Alex Scott; and Spectral Graph Theory, based on the article of Joe Neeman, Charles Radin and Lorenzo Sadun. These two approaches manage to find the behavior of the tail in two different regimes. In this dissertation we give an overview of the article of Goldschmidt, Griffiths and Scott, discuss in detail the article of artigo Neeman, Radin and Sadun. In particular, we shall explore the connection between the lower tail of the number of triangles and the behavior of the most negative eigenvalues of the adjacency matrix. We shall see that the triangle count tends to especially depend on the most negative eigenvalue.
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