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Message-Passing Algorithms for Sparse Network Alignment MOHSEN BAYATI, Stanford University DAVID F. GLEICH, Purdue University AMIN SABERI, Stanford University YING WANG, Google Network alignment generalizes and unifies several approaches for forming a matching or alignment between the vertices of two graphs. We study a mathematical programming framework for network alignment problem and a sparse variation of it where only a small number of matches between the vertices of the two graphs are possible. We propose a new message passing algorithm that allows us to compute, very efficiently, approximate solutions to the sparse network alignment problems with graph sizes as large as hundreds of thousands of vertices. We also provide extensive simulations comparing our algorithms with two of the best solvers for network alignment problems on two synthetic matching problems, two bioinformatics problems, and three large ontology alignment problems including a multilingual problem with a known labeled alignment. Categories and Subject Descriptors: H.4.0 [Information Systems Applications]: General General Terms: Algorithms Additional Key Words and Phrases: Network alignment, graph matching, belief propagation, message-passing ACM Reference Format: Bayati, M., Gleich, D. F., Saberi, A., and Wang, Y. 2013. Message-passing algorithms for sparse network alignment. ACM Trans. Knowl. Discov. Data 7, 1,
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Mar 1, 2013
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