Color distribution can accelerate network alignment

Aligning a query network to an arbitrary large target network while ensuring provable optimality guarantee is a computationally challenging task. To ensure the confidence in the optimality of the alignment, existing methods often use an iterative randomization technique called color coding. Each iteration of the color coding technique employs dynamic programming that is exponential in the number of nodes in the query network. Here, we develop a method named ColT (Colorful Tree) that reduces the cost of this bottleneck. It particularly focuses on query networks with tree topology which is considered frequently in the literature. ColT exploits the topology of the query tree and uses the color distribution in the target network to filter unpromising alignments without compromising the confidence in the optimality. We experiment on a comprehensive set of synthetic and real data sets. ColT demonstrates supremacy over the state-of-the-art color coding algorithm, QNet with growing size of the query trees. For query trees of nine nodes in directed and undirected target networks, ColT outperforms QNet by factors of eight and fifteen, respectively. Our experiments also suggest that ColT identifies functionally similar regions in protein-protein interaction networks.