Answers are grounded in this paper and cite their sources.
Theoretical analysis establishes eigenvalue-derived lower bounds for graph partitioning across random networks, indicating tight spectral limits on minimum edge cuts.
Let a k-partition of a graph be a division of the vertices into k disjoint subsets containing m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ≥ m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , …, ≥ m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> vertices. Let E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> be the number of edges whose two vertices belong to different subsets. Let λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ≥ λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , …, ≥ λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> be the k largest eigenvalues of a matrix, which is the sum of the adjacency matrix of the graph plus any diagonal matrix U such that the sum of all the elements of the sum matrix is zero. Then given equation. A theorem is given that shows the effect of the maximum degree of any node being limited, and it is also shown that the right-hand side is a concave function of U. Computational studies are made of the ratio of upper bound to lower bound for the two-partition of a number of random graphs having up to 100 nodes.
No takes yet. Share an insight, caveat, or question.
Donath et al. (1973) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: