Spectral Clustering for Divide-and-Conquer Graph Matching

We present a novel divide-and-conquer bijective graph matching algorithm.The algorithm is fully parallelizable, and scales to match \"big data\" graphs.We demonstrate the effectiveness of the algorithm by matching DTMRI human connectomes. We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic block model random graphs are correctly matched utilizing very few seeds through our divide-and-conquer procedure. We also demonstrate the effectiveness of our approach in matching very large graphs in simulated and real data examples, showing up to a factor of 8 improvement in runtime with minimal sacrifice in accuracy.