On Consistent Vertex Nomination Schemes
Authors: Vince Lyzinski, Keith Levin, Carey E. Priebe
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | Vince Lyzinski EMAIL Department of Mathematics and Statistics University of Massachusetts Amherst Amherst, MA 01003, USA Keith Levin EMAIL Department of Statistics University of Michigan Ann Arbor, MI 48109, USA Carey E. Priebe EMAIL Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore, MD 21218-2608, USA |
| Pseudocode | No | The paper describes methods and definitions in a mathematical and theoretical manner. It does not contain any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper introduces theoretical models and discusses their properties through conceptual examples (e.g., 'Example 1 (R -ER(P))', 'Example 3 (Independent Erd os-R enyi graphs)'), but it does not use or provide access information for any specific empirical datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, therefore, no dataset splits are discussed or provided. |
| Hardware Specification | No | The paper describes theoretical work and does not detail any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on any implementation or experimental results, therefore no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical, focusing on definitions, derivations, and proofs, and does not include details of an experimental setup, hyperparameters, or training configurations. |