PMC:4331682 / 20357-21966 JSONTXT

{"project":"2_test","denotations":[{"id":"25707987-23874484-14887596","span":{"begin":170,"end":172},"obj":"23874484"},{"id":"25707987-23874484-14887597","span":{"begin":425,"end":427},"obj":"23874484"},{"id":"25707987-23874484-14887598","span":{"begin":770,"end":772},"obj":"23874484"}],"text":"Constructing the multiple network alignment\nOnce we have computed the node correspondence scores in (8) for every pair of networks in G, we take a greedy approach as in [12] to construct the multiple network alignment. The overall alignment process is as follows. First, in order to improve the reliability of the node correspondence scores, we selectively apply the probabilistic consistent transformation (PCT) defined in [12]. If λ is larger than a predefined threshold λt, we do not apply PCT to the node correspondence scores. A large λ implies that the product graph is ill connected (e.g., containing a large number of isolated nodes), in which case applying the PCT would not be helpful and may in fact make the scores less reliable. This is because the PCT in [12] was developed based on the assumption that the product graphs for all network pairs are relatively well connected. After the potential score refinement step through PCT, we begin with an empty alignment and greedily add aligned node pairs (ui, vj) to the network alignment, starting from the pairs with the highest node correspondence scores, until there is no other node pair left that can be added without creating inconsistencies in the network alignment. Assuming that the node correspondence scores in (8) obtained by the context-sensitive random walk model with restart accurately reflect the true correspondence between nodes - such that the score is proportional to the posterior node alignment probability - the proposed network alignment scheme can be viewed as a heuristic way to find the MEA alignment of the networks in G."}