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The Ethereal
Graph pattern detection: Hardness for all induced patterns and faster non-induced cycles
April 07, 2019 ยท The Ethereal ยท ๐ Symposium on the Theory of Computing
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Authors
Mina Dalirrooyfard, Thuy Duong Vuong, Virginia Vassilevska Williams
arXiv ID
1904.03741
Category
cs.CC: Computational Complexity
Cross-listed
cs.DS
Citations
46
Venue
Symposium on the Theory of Computing
Last Checked
1 month ago
Abstract
We consider the pattern detection problem in graphs: given a constant size pattern graph $H$ and a host graph $G$, determine whether $G$ contains a subgraph isomorphic to $H$. Our main results are: * We prove that if a pattern $H$ contains a $k$-clique subgraph, then detecting whether an $n$ node host graph contains a not necessarily induced copy of $H$ requires at least the time for detecting whether an $n$ node graph contains a $k$-clique. The previous result of this nature required that $H$ contains a $k$-clique which is disjoint from all other $k$-cliques of $H$. * We show that if the famous Hadwiger conjecture from graph theory is true, then detecting whether an $n$ node host graph contains a not necessarily induced copy of a pattern with chromatic number $t$ requires at least the time for detecting whether an $n$ node graph contains a $t$-clique. This implies that: (1) under Hadwiger's conjecture for every $k$-node pattern $H$, finding an induced copy of $H$ requires at least the time of $\sqrt k$-clique detection, and at least size $ฯ(n^{\sqrt{k}/4})$ for any constant depth circuit, and (2) unconditionally, detecting an induced copy of a random $G(k,p)$ pattern w.h.p. requires at least the time of $ฮ(k/\log k)$-clique detection, and hence also at least size $n^{ฮฉ(k/\log k)}$ for circuits of constant depth. * Finally, we consider the case when the pattern is a directed cycle on $k$ nodes, and we would like to detect whether a directed $m$-edge graph $G$ contains a $k$-Cycle as a not necessarily induced subgraph. We resolve a 14 year old conjecture of [Yuster-Zwick SODA'04] on the complexity of $k$-Cycle detection by giving a tight analysis of their $k$-Cycle algorithm. Our analysis improves the best bounds for $k$-Cycle detection in directed graphs, for all $k>5$.
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