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4 in the Appendix). Under GHOST’s NCF, WAVE is always superior to MI-GRAAL’s AS (G-W is better than G-M), and WAVE is superior to GHOST’s AS (G-W is better than G-G) with respect to two of the four measures (edge-based S3 and LCCS), while GHOST’s AS is superior (G-G is better than G-W) with respect to the other two measures (node-based NC and Exp-GO) (Figs. 4 in the Appendix). Hence, WAVE and GHOST’s AS are comparable overall. Again, WAVE in general works better under MI-GRAAL’s NCF than under GHOST’s, as M-W is overall superior to G-W.

Recall that a key novelty of WAVE is that while optimizing edge conservation (in addition to node conservation), WAVE weighs each conserved edge to favor aligning edges with highly NCF-similar end nodes. , M-W(U) and G-W(U)). As we will show, the edge-weighted versions are superior. , βn and βe ) on WAVE’s results. , equally favoring node and edge conservation, is superior to other parameter variations. Next, using the edge-weighted versions of WAVE with βn = 1 and βe = 1, we evaluate the five aligners (M-M, M-W, G-M, G-G, and G-W) against each other.

Best Alignments. Under MI-GRAAL’s NCF, WAVE is always superior (M-W is better than M-M) with respect to S3 , and it is almost always superior with respect to LCCS as well as Exp-GO (Figs. 5 in the Appendix). Hence, here WAVE is even more superior than for topological alignments only. Under GHOST’s NCF, WAVE is superior to MI-GRAAL’s AS (as G-W is better than G-M) in most cases for each of S3 and Exp-GO, and in some cases for LCCS. Also, here WAVE is overall superior to GHOST’s AS (G-W is better than G-G) with respect to Exp-GO but not with respect to S3 or LCCS (Figs.

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