If you are using MAGNA++, please reference both of the following papers:
1. Vikram Saraph and Tijana Milenkovic, MAGNA: Maximizing Accuracy in Global Network Alignment, Bioinformatics, 30(20), 2931–2940, 2014.
2. Vipin Vijayan, Vikram Saraph, and Tijana Milenkovic, MAGNA++: Maximizing Accuracy in Global Network Alignment via both node and edge conservation, Bioinformatics, DOI: 10.1093/bioinformatics/btv161, 2015.

MAGNA++ is our software tool for pairwise global network alignment. It is an extension of MAGNA. An example of an application domain where MAGNA++ is useful is computational biology - MAGNA++ can be used for biological network alignment.

Biological network alignment aims to identify similar regions between networks of different species. Existing methods compute node similarities to rapidly identify from possible alignments the high-scoring alignments with respect to the overall node similarity (i.e. node conservation). But, the accuracy of the alignments is then evaluated with some other measure that is different than the node similarity used to construct the alignments. Typically, one measures the amount of conserved edges. Thus, the existing methods align similar nodes between networks hoping to conserve many edges (after the alignment is constructed!). Instead, MAGNA directly ‘optimizes’ edge conservation while the alignment is constructed, without decreasing the quality of node mapping. MAGNA appears in the following publication: Vikram Saraph and Tijana Milenkovic, MAGNA: Maximizing Accuracy in Global Network Alignment, Bioinformatics, 30(20), 2931–2940, 2014.

MAGNA uses a genetic algorithm and our novel alignment crossover function to construct superior alignments from scratch while trying to optimize a measure of alignment quality determined by the user. MAGNA can optimize three alignment quality topological measures of edge conservation: symmetric substructure score (S³), edge correctness (EC), induced conserved structure (ICS). (We recommend optimizing S³, as the superior of the three measures - see the MAGNA paper for details.) In addition to producing superior alignments from scratch, MAGNA is also capable of improving the quality of alignments generated by existing state-of-the-art methods, such as IsoRank, MI-GRAAL, or GHOST, in terms of both node and edge conservation as well as both topological and biological alignment accuracy. The executables and source code of MAGNA are available for download, along with the data from the paper.

MAGNA++ extends MAGNA in four ways. 1) It simultaneously maximizes any one of three different measures of edge conservation and any desired node conservation measure, which further improves alignment quality compared to maximizing only node conservation or only edge conservation. 2) It parallelizes the original MAGNA algorithm to automatically use all available resources and thus speed up computation. 3) It provides a friendly graphical user interface for easy use by domain (e.g., biological) scientists. 4) At the same time, MAGNA++ offers source code for easy extensibility by computational scientists. MAGNA++ reference is: Vipin Vijayan, Vikram Saraph, and Tijana Milenkovic, MAGNA++: Maximizing Accuracy in Global Network Alignment via both node and edge conservation, Bioinformatics, DOI: 10.1093/bioinformatics/btv161, 2015.

The executables and source code of MAGNA++ are available for download, along with detailed usage instructions. You can also download the data from the paper.

Vipin Vijayan may be contacted via email at vvijayan [at] nd [dot] edu and Tijana Milenkovic can be contacted via email at tmilenko [at] nd [dot] edu.