A Tight Bound on Localization of Electrical Flows
This provides a tight bound for a fundamental quantity in electrical flow theory, improving prior work for graph theorists and algorithm designers.
The paper proves that for any unweighted graph on n vertices, the L1 norm of a unit electric current between the endpoints of a random edge is at most 2 log n, and for weighted graphs the spectral norm of the absolute transfer-current matrix is at most 2 log n, improving the previous O(log^2 n) bound.
We prove that for any unweighted graph on n vertices the L1 norm of a unit electric current between the endpoints of a random edge is at most 2 log n. Furthermore, we show that on any weighted graph the spectral norm of the entry-wise absolute value of the symmetric transfer-current matrix is at most 2 log n. This bound is tight up to constants and improves the O(log^2 n) bound from [Schild-Rao-Srivastava, SODA '18]. The initial proofs were generated by OpenAI's ChatGPT 5.5 Pro; the authors have verified and rewritten them to enhance readability and provide additional context.