Multimodal Network Alignment | Proceedings of the 2017 SIAM International Conference on Data Mining | Society for Industrial and Applied Mathematics

A multimodal network encodes relationships between the same set of nodes in multiple settings, and network alignment is a powerful tool for transferring information and insight between a pair of networks. We propose a method for multimodal network alignment that computes a matrix which indicates the alignment, but produces the result as a lowrank factorization directly. We then propose new methods to compute approximate maximum weight matchings of lowrank matrices to produce an alignment. We evaluate our approach by applying it on synthetic networks and use it to de-anonymize a multimodal transportation network.