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        <datestamp>2024-03-06T10:37:33Z</datestamp>
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          <dc:title>Graph Motif Problems Parameterized by Dual</dc:title>
          <dc:creator>Fertin, Guillaume</dc:creator>
          <dc:creator>Komusiewicz, Christian</dc:creator>
          <dc:subject>NP-hard problem</dc:subject>
          <dc:subject>subgraph problem</dc:subject>
          <dc:subject>fixed-parameter algorithm</dc:subject>
          <dc:subject>lowerbounds</dc:subject>
          <dc:subject>kernelization</dc:subject>
          <dc:description>Let G=(V,E) be a vertex-colored graph, where C is the set of colors used to color V. The Graph Motif (or GM) problem takes as input G, a multiset M of colors built from C, and asks whether there is a subset S subseteq V such that (i) G[S] is connected and (ii) the multiset of colors obtained from S equals M. The Colorful Graph Motif problem (or CGM) is a constrained version of GM in which M=C, and the List-Colored Graph Motif problem (or LGM) is the extension of GM in which each vertex v of V may choose its color from a list L(v) of colors.&#13;
&#13;
We study the three problems GM, CGM and LGM, parameterized by l:=|V|-|M|. In particular, for general graphs, we show that, assuming the strong exponential-time hypothesis, CGM has no (2-epsilon)^l * |V|^{O(1)}-time algorithm, which implies that a previous algorithm, running in O(2^l\cdot |E|) time is optimal. We also prove that LGM is W[1]-hard even if we restrict ourselves to lists of at most two colors. If we constrain the input graph to be a tree, then we show that, in contrast to CGM, GM can be solved in O(4^l *|V|) time but admits no polynomial kernel, while CGM can be solved in O(sqrt{2}^l + |V|) time and admits a polynomial kernel.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Guillaume Fertin and Christian Komusiewicz</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 54, 27th Annual Symposium on Combinatorial Pattern Matching (CPM 2016)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CPM.2016.7</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-60837</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2016.7</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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