Average-case and smoothed analysis of graph isomorphism
Julia Gaudio, Mikl\'os Z. R\'acz, Anirudh Sridhar

TL;DR
This paper introduces a simple local algorithm for graph isomorphism that works efficiently on sparse graphs and demonstrates that small random perturbations can make the problem easier, showing polynomial smoothed complexity.
Contribution
It proves new bounds for when local neighborhoods suffice for graph isomorphism and establishes smoothed analysis results for the problem.
Findings
3-neighborhoods identify isomorphism classes above the connectivity threshold
Small random perturbations typically enable canonical labeling
Graph isomorphism has polynomial smoothed complexity
Abstract
We propose a simple and efficient local algorithm for graph isomorphism which succeeds for a large class of sparse graphs. This algorithm produces a low-depth canonical labeling, which is a labeling of the vertices of the graph that identifies its isomorphism class using vertices' local neighborhoods. Prior work by Czajka and Pandurangan showed that the degree profile of a vertex (i.e., the sorted list of the degrees of its neighbors) gives a canonical labeling with high probability when (and ); subsequently, Mossel and Ross showed that the same holds when . We first show that their analysis essentially cannot be improved: we prove that when , with high probability there exist distinct vertices with isomorphic -neighborhoods. Our first main…
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Taxonomy
TopicsAdvanced Graph Theory Research · Complexity and Algorithms in Graphs · Interconnection Networks and Systems
