Operator theory of electrical resistance networks
Palle E. T. Jorgensen, Erin P. J. Pearse

TL;DR
This paper develops an operator-theoretic framework for resistance networks, embedding them into a Hilbert space of finite energy functions, and explores their boundary, spectral, and probabilistic properties, with applications to physical models.
Contribution
It introduces a novel Hilbert space embedding of resistance networks, analyzes their boundaries, and connects the theory to Gaussian processes and spectral representations, advancing understanding of network analysis.
Findings
Embedded resistance networks form a reproducing kernel Hilbert space.
Resistance networks with nonconstant harmonic functions have a boundary structure.
Spectral analysis reveals nontrivial boundaries linked to operator defect.
Abstract
A resistance network is a weighted graph with intrinsic (resistance) metric . We embed the resistance network into the Hilbert space of functions of finite energy. We use the resistance metric to study , and vice versa and show that the embedded images of the vertices form a reproducing kernel for this Hilbert space. We also obtain a discrete version of the Gauss-Green formula for resistance networks and show that resistance networks which support nonconstant harmonic functions of finite energy have a certain type of \emph{boundary}. We obtain an analytic boundary representation for the harmonic functions of finite energy in a sense analogous to the Poisson or Martin boundary representations, but with different hypotheses, and for a different class of functions. In the process, we construct a dense space of…
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Taxonomy
TopicsMathematical Analysis and Transform Methods · Topological and Geometric Data Analysis · Neural Networks and Applications
