Abstract
Motif discovery is the problem of finding subgraphs of a network that appear surprisingly often. Each such subgraph may indicate a small-scale interaction feature in applications ranging from a genomic interaction network, a significant relationship involving rock musicians, or any other application that can be represented as a network. We look at the problem of constrained search for motifs based on labels (e.g. gene ontology, musician type to continue our example from above). This chapter presents a brief review of the state of the art in motif finding and then extends the gTrie data structure from Ribeiro and Silva (Data Min Knowl Discov 28(2):337–377, 2014b) to support labels. Experiments validate the usefulness of our structure for small subgraphs, showing that we recoup the cost of the index after only a handful of queries.
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Acknowledgements
Shasha’s work has been partially supported by an INRIA International Chair and the U.S. National Science Foundation under grants MCB-1412232, IOS-1339362, MCB-1355462, MCB-1158273, IOS-0922738, and MCB-0929339. This support is greatly appreciated.
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Mongioví, M., Micale, G., Ferro, A., Giugno, R., Pulvirenti, A., Shasha, D. (2018). gLabTrie: A Data Structure for Motif Discovery with Constraints. In: Fletcher, G., Hidders, J., Larriba-Pey, J. (eds) Graph Data Management. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-96193-4_3
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DOI: https://doi.org/10.1007/978-3-319-96193-4_3
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