
AbstractWe study the problem of matching a string in a labeled graph. Previous research has shown that unless theOrthogonal Vectors Hypothesis(OVH) is false, one cannot solve this problem in strongly sub-quadratic time, nor index the graph in polynomial time to answer queries efficiently (Equi et al. ICALP 2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic graphs with binary alphabet, but there naturally exist also graph classes that are easy to index: For example,Wheeler graphs(Gagie et al. Theor. Comp. Sci.2017) cover graphs admitting a Burrows-Wheeler transform -based indexing scheme. However, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney, Thankachan, ESA 2019). We propose an approach to alleviate the construction bottleneck of Wheeler graphs. Rather than starting from an arbitrary graph, we study graphs induced frommultiple sequence alignments().Elastic degenerate strings(Bernadini et al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce here their generalization:elastic founder graphs. We first prove that even such induced graphs are hard to index under OVH. Then we introduce two subclasses, repeat-free and semi-repeat-free graphs, that are easy to index. We give a linear time algorithm to construct a repeat-free (non-elastic) founder graph from a gapless , and (parameterized) near-linear time algorithms to construct a semi-repeat-free (repeat-free, respectively) elastic founder graph from general . Finally, we show that repeat-free founder graphs admit a reduction to Wheeler graphs in polynomial time.
Segmentation algorithms, graph algorithms, FOS: Computer and information sciences, Data structures, string matching, Analysis of algorithms and problem complexity, E.4, [INFO] Computer Science [cs], Computational Complexity (cs.CC), Algorithms on strings, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Graph algorithms, Computer and information sciences, computational complexity, Pangenomics, E.1; E.4; F.1.3; F.2.2, Compressed data structures, pangenomics, Computational complexity, compressed data structures, Computer Science - Computational Complexity, segmentation algorithms, Graph theory (including graph drawing) in computer science, multiple sequence alignment, String matching, Multiple sequence alignment, E.1, F.1.3, F.2.2, Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science)
Segmentation algorithms, graph algorithms, FOS: Computer and information sciences, Data structures, string matching, Analysis of algorithms and problem complexity, E.4, [INFO] Computer Science [cs], Computational Complexity (cs.CC), Algorithms on strings, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Graph algorithms, Computer and information sciences, computational complexity, Pangenomics, E.1; E.4; F.1.3; F.2.2, Compressed data structures, pangenomics, Computational complexity, compressed data structures, Computer Science - Computational Complexity, segmentation algorithms, Graph theory (including graph drawing) in computer science, multiple sequence alignment, String matching, Multiple sequence alignment, E.1, F.1.3, F.2.2, Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science)
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| 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. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
