We sketch the history of spectral ranking—a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google's PageRank algorithm, spectral ranking was devised more than 60 years ago, almost exactly in the same terms, and has been studied in psychology, social sciences, bibliometrics, economy, and choice theory. We describe the contribution given by previous scholars in precise and modern mathematical terms: Along the way, we show how to express in a general way damped rankings, such as Katz's index, as dominant eigenvectors of perturbed matrices, and then use results on the Drazin inverse to go back to the dominant eigenvectors by a limit process. The result suggests a regularized definition of spectral ranking that yields for a general matrix a unique vector depending on a boundary condition.

The paper characterizes matrices which have a given system of vectors orthogonal with respect to a given probability distribution as its right eigenvectors. Results of Hoare and Rahman are unified in this context, then all matrices with a given orthogonal polynomial system as right eigenvectors under the constraint a0j = 0 for j ≥ 2 are specified. The only stochastic matrices P = {pij} satisfying p00 + p01 = 1 with the Hahn polynomials as right eigenvectors have the form of the Moran mutation model.

We consider some geometric properties of eigenvectors of linear operators on infinite dimensional Hilbert space. It is proved that the property of a family of vectors $({{x}_{\text{n}}})$ to be eigenvectors $T{{x}_{n}}={{\lambda }_{n}}{{x}_{n}}({{\lambda }_{n}}\ne {{\lambda }_{k}}\text{for}\,n\ne k)$ of a bounded operator $T$ (admissibility property) is very instable with respect to additive and linear perturbations. For instance, (1) for the sequence ${{\left( {{x}_{n}}+{{\epsilon }_{n}}{{v}_{n}} \right)}_{n\ge k(\epsilon )}}$ to be admissible for every admissible $({{x}_{\text{n}}})$ and for a suitable choice of small numbers ${{\epsilon }_{n}}\,\ne \,0$ it is necessary and sufficient that the perturbation sequence be eventually scalar: there exist ${{\text{ }\!\!\gamma\!\!\text{ }}_{n}}\,\in \,C$ such that ${{v}_{n}}\,=\,{{\gamma }_{n}}{{v}_{k}}\,\text{for}\,n\,\ge \,\text{k}$ (Theorem 2); (2) for a bounded operator $A$ to transform admissible families $({{x}_{\text{n}}})$ into admissible families $(A{{x}_{n}})$ it is necessary and sufficient that $A$ be left invertible (Theorem 4).

The numerical taxonomic analysis of encrinurine trilobites by Temple and Tripp (1979) is extended to 28 described Chinese species, 35 other Chinese samples, and 13 non-Chinese species, in addition to 56 species analysed previously. The 132 entities are coded for 34 cranidial and pygidial attributes, and are ordinated on the eigenvectors of the interattribute correlation matrix for 32 attributes. Chinese material falls into four clusters informally compared with Coronocephalus, Kailia, Encrinuroides and Encrinurus. The Encrinurus-like Chinese species occur mainly in Northern China and have links with non-Chinese species.