Telling Stories Fast
This paper presents a linear-time delay algorithm for enumerating all directed acyclic subgraphs of a directed graph G(V,E) that have their sources and targets included in two subsets S and T of V, respectively. From these subgraphs, called pitches, the maximal ones, called stories, may be extracted in a dramatically more efficient way in relation to a previous story telling algorithm. The improvement may even increase if a pruning technique is further applied that avoids generating many pitches which have no chance to lead to a story. We experimentally demonstrate these statements by making use of a quite large dataset of real metabolic pathways and networks.
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- 1.Acuña, V., Birmelé, E., Cottret, L., Crescenzi, P., Jourdan, F., Lacroix, V., Marchetti-Spaccamela, A., Marino, A., Milreu, P.V., Sagot, M.F., Stougie, L.: Telling stories: Enumerating maximal directed acyclic graphs with a constrained set of sources and targets. Theor. Comput. Sci. 457, 1–9 (2012)zbMATHCrossRefGoogle Scholar
- 5.Uno, T.: Two general methods to reduce delay and change of enumeration algorithms. NII Technical Report (2003)Google Scholar
- 6.Milreu, P.V.: Enumerating Functional Substructures of Genome-Scale Metabolic Networks: Stories, Precursors and Organisations. PhD thesis, Université Claude Bernard, Lyon 1, France (2012)Google Scholar
- 7.Milreu, P.V., Acuña, V., Birmelé, E., Borassi, M., Cottret, L., Junot, C., Klein, C., Marchetti-Spaccamela, A., Marino, A., Stougie, L., Jourdan, F., Lacroix, V., Crescenzi, P., Sagot, M.-F.: Metabolic stories: exploring all possible scenarios for metabolomics data analysi (in preparation, 2013)Google Scholar