A common variable minimax theorem for graphs
Not on the map. Why?
Abstract (arXiv)
Let G = G1 = (V, E1), dots, Gm = (V, Em) be a collection of m graphs defined on a common set of vertices V but with different edge sets E1, dots, Em. Informally, a function f :V rightarrow ℝ is smooth with respect to Gk = (V,Ek) if f(u) ~ f(v) whenever (u, v) ∈ Ek. We study the problem of understanding whether there exists a nonconstant function that is smooth with respect to all graphs in G, simultaneously, and how to find it if it exists.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 16 references that are in the map. First posted on arXiv 30 Jul 2021.
Builds on
2 of 16 references.
References · 2
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2022Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scalingcited by 7
- 2022Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operatorscited by 6
- 2024On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among themcited by 2
- 2024Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generationcited by 1
- 2026Complex Interpolation of Matrices with an application to Multi-Manifold Learningcited by 0
- 2026Complex Interpolation of Matrices with an application to Multi-Manifold Learning15 Apr 2026
- 2024Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation27 Aug 2024
- 2024On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them10 Jun 2024
- 2022Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling16 Sep 2022
- 2022Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators21 Jan 2022