MultiMotif is a software for finding statistically significant labeled motifs in multi-relational networks with analytically derived p-values. MultiMotif uses a custom version of RI algorithm (Bonnici et al., 2013) for counting occurrences of labeled motifs in a graph and implements an analytical model to assess motifs significance without generating random graphs. MultiMotif works on both directed and undirected networks and handle non-induced labeled motifs.
Download
Before downloading, be sure to have Java installed (version 8 or more).
Software
JAR file of MultiMotif :
Source code
JAVA source code for MultiMotif :
Auxiliary files
Sample networks:
Documentation
To run MultiMotif please refer to the README.txt files contained in the relative ZIP archives.
References
If you use MultiMotif for reasearch purpose, please cite the following paper:
- Micale G, Pulvirenti A, Ferro A, Giugno R, Shasha D (2019). Fast methods for finding significant motifs on labelled multi-relational networks. Journal of Complex Networks, doi:10.1093/comnet/cnz008
About
Authors
Dept. of Clinical and Experimental Medicine
University of Catania
gmicale@dmi.unict.it Rosalba Giugno
Dept. of Computer Science
University of Verona
rosalba.giugno@univr.it Alfredo Ferro
Dept. of Clinical and Experimental Medicine
University of Catania
ferro@dmi.unict.it
Dept. of Computer Science
Courant Institute of Mathematical Sciences
New York University
shasha@cs.nyu.edu Alfredo Pulvirenti
Dept. of Clinical and Experimental Medicine
University of Catania
apulvirenti@dmi.unict.it
Contacts
If you have any trouble with the software or you want to report any bug, please contact: gmicale@dmi.unict.it and apulvirenti@dmi.unict.it
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