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https://doi.org/10.1137/1.9781...
Article . 2013 . Peer-reviewed
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Article . 2014
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Hardness of Robust Graph Isomorphism, Lasserre Gaps, and Asymmetry of Random Graphs

Authors: Ryan O'Donnell; John Wright 0004; Chenggang Wu 0003; Yuan Zhou 0007;

Hardness of Robust Graph Isomorphism, Lasserre Gaps, and Asymmetry of Random Graphs

Abstract

Building on work of Cai, F��rer, and Immerman \cite{CFI92}, we show two hardness results for the Graph Isomorphism problem. First, we show that there are pairs of nonisomorphic $n$-vertex graphs $G$ and $H$ such that any sum-of-squares (SOS) proof of nonisomorphism requires degree $��(n)$. In other words, we show an $��(n)$-round integrality gap for the Lasserre SDP relaxation. In fact, we show this for pairs $G$ and $H$ which are not even $(1-10^{-14})$-isomorphic. (Here we say that two $n$-vertex, $m$-edge graphs $G$ and $H$ are $��$-isomorphic if there is a bijection between their vertices which preserves at least $��m$ edges.) Our second result is that under the {\sc R3XOR} Hypothesis \cite{Fei02} (and also any of a class of hypotheses which generalize the {\sc R3XOR} Hypothesis), the \emph{robust} Graph Isomorphism problem is hard. I.e.\ for every $��> 0$, there is no efficient algorithm which can distinguish graph pairs which are $(1-��)$-isomorphic from pairs which are not even $(1-��_0)$-isomorphic for some universal constant $��_0$. Along the way we prove a robust asymmetry result for random graphs and hypergraphs which may be of independent interest.

Keywords

FOS: Computer and information sciences, Computer Science - Computational Complexity, Computational Complexity (cs.CC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Average
Green