Spectral plots and the representation and interpretation of biological data

It is basic question in biology and other fields to identify the char- acteristic properties that on one hand are shared by structures from a particular realm, like gene regulation, protein-protein interaction or neu- ral networks or foodwebs, and that on the other hand distinguish them from other structures. We introduce and apply a general method, based on the spectrum of the normalized graph Laplacian, that yields repre- sentations, the spectral plots, that allow us to find and visualize such properties systematically. We present such visualizations for a wide range of biological networks and compare them with those for networks derived from theoretical schemes. The differences that we find are quite striking and suggest that the search for universal properties of biological networks should be complemented by an understanding of more specific features of biological organization principles at different scales.Comment: 15 pages, 7 figure

Neighborhood properties of complex networks

A concept of neighborhood in complex networks is addressed based on the criterion of the minimal number os steps to reach other vertices. This amounts to, starting from a given network $R_1$, generating a family of networks $R_\ell, \ell=2,3,...$ such that, the vertices that are $\ell$ steps apart in the original $R_1$, are only 1 step apart in $R_\ell$. The higher order networks are generated using Boolean operations among the adjacency matrices $M_\ell$ that represent $R_\ell$. The families originated by the well known linear and the Erd\"os-Renyi networks are found to be invariant, in the sense that the spectra of $M_\ell$ are the same, up to finite size effects. A further family originated from small world network is identified

q-Newton binomial: from Euler to Gauss

A counter-intuitive result of Gauss (formulae (1.6), (1.7) below) is made less mysterious by virtue of being generalized through the introduction of an additional parameter