Literature DB >> 21118772

Graph characterization via Ihara coefficients.

Peng Ren1, Richard C Wilson, Edwin R Hancock.   

Abstract

The novel contributions of this paper are twofold. First, we demonstrate how to characterize unweighted graphs in a permutation-invariant manner using the polynomial coefficients from the Ihara zeta function, i.e., the Ihara coefficients. Second, we generalize the definition of the Ihara coefficients to edge-weighted graphs. For an unweighted graph, the Ihara zeta function is the reciprocal of a quasi characteristic polynomial of the adjacency matrix of the associated oriented line graph. Since the Ihara zeta function has poles that give rise to infinities, the most convenient numerically stable representation is to work with the coefficients of the quasi characteristic polynomial. Moreover, the polynomial coefficients are invariant to vertex order permutations and also convey information concerning the cycle structure of the graph. To generalize the representation to edge-weighted graphs, we make use of the reduced Bartholdi zeta function. We prove that the computation of the Ihara coefficients for unweighted graphs is a special case of our proposed method for unit edge weights. We also present a spectral analysis of the Ihara coefficients and indicate their advantages over other graph spectral methods. We apply the proposed graph characterization method to capturing graph-class structure and clustering graphs. Experimental results reveal that the Ihara coefficients are more effective than methods based on Laplacian spectra.

Mesh:

Year:  2010        PMID: 21118772     DOI: 10.1109/TNN.2010.2091969

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  1 in total

1.  Spectral redemption in clustering sparse networks.

Authors:  Florent Krzakala; Cristopher Moore; Elchanan Mossel; Joe Neeman; Allan Sly; Lenka Zdeborová; Pan Zhang
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-25       Impact factor: 11.205

  1 in total

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