On consistent vertex nomination schemes

TL;DR

This paper extends the vertex nomination problem to a broad statistical graph model, defining optimality and consistency, and proves the non-existence of universally consistent schemes, with practical examples.

Contribution

It generalizes the vertex nomination framework, introduces formal definitions of optimality and consistency, and establishes fundamental impossibility results.

Findings

Bayes optimal vertex nomination scheme derived

No universally consistent schemes exist

Framework applicable to broad graph models

Abstract

Given a vertex of interest in a network $G_1$, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network $G_2$. A vertex nomination scheme produces a list of the vertices in $G_2$, ranked according to how likely they are judged to be the corresponding vertex of interest in $G_2$. The vertex nomination problem and related information retrieval tasks have attracted much attention in the machine learning literature, with numerous applications to social and biological networks. However, the current framework has often been confined to a comparatively small class of network models, and the concept of statistically consistent vertex nomination schemes has been only shallowly explored. In this paper, we extend the vertex nomination problem to a very general statistical model of graphs. Further, drawing inspiration from the long-established classification framework in the pattern recognition literature, we provide definitions for the key notions of Bayes optimality and consistency in our extended vertex nomination framework, including a derivation of the Bayes optimal vertex nomination scheme. In addition, we prove that no universally consistent vertex nomination schemes exist. Illustrative examples are provided throughout.