Refinement of Metrics: Erd\H{o}s Number, a Case Study
K.Lock, W.Y.Pong, A. Wittmond

TL;DR
This paper introduces the concept of refinement to improve metrics, specifically refining the Erdős number by applying new methods to the collaboration graph's shortest-path distance.
Contribution
It proposes two novel methods for refining metrics and applies them to generate new versions of the Erdős number based on the collaboration graph.
Findings
Developed two methods for metric refinement.
Produced several refined Erdős numbers.
Enhanced understanding of collaboration distances.
Abstract
We introduce a concept called refinement and develop two different ways of refining metrics. By applying these methods we produce several refinements of the shortest-path distance on the collaboration graph and hence a couple new versions of the Erd\H{o}s number.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Geometry and Mesh Generation
