4 research outputs found
Addressing Computational Bottlenecks in Higher-Order Graph Matching with Tensor Kronecker Product Structure
Graph matching, also known as network alignment, is the problem of finding a
correspondence between the vertices of two separate graphs with strong
applications in image correspondence and functional inference in protein
networks. One class of successful techniques is based on tensor Kronecker
products and tensor eigenvectors. A challenge with these techniques are memory
and computational demands that are quadratic (or worse) in terms of problem
size. In this manuscript we present and apply a theory of tensor Kronecker
products to tensor based graph alignment algorithms to reduce their runtime
complexity from quadratic to linear with no appreciable loss of quality. In
terms of theory, we show that many matrix Kronecker product identities
generalize to straightforward tensor counterparts, which is rare in tensor
literature. Improved computation codes for two existing algorithms that utilize
this new theory achieve a minimum 10 fold runtime improvement.Comment: 14 pages, 2 pages Supplemental, 5 figure