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.
glabtrie: A data structure for motif discovery with constraints
Misael;
2018
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
