Publications [#363385] of Lawrence Carin

Papers Published

1. Xu, H; Luo, D; Carin, L; Zha, H, Learning Graphons via Structured Gromov-Wasserstein Barycenters, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 12A (January, 2021), pp. 10505-10513, ISBN 9781713835974 [doi]
   (last updated on 2024/12/31)

   Abstract:
   We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory of graphons, we leverage a step function to approximate a graphon. We show that the cut distance of graphons can be relaxed to the Gromov-Wasserstein distance of their step functions. Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs. Furthermore, we develop several enhancements and extensions of the basic algorithm, e.g., the smoothed Gromov-Wasserstein barycenter for guaranteeing the continuity of the learned graphons and the mixed Gromov-Wasserstein barycenters for learning multiple structured graphons. The proposed approach overcomes drawbacks of prior state-of-the-art methods, and outperforms them on both synthetic and real-world data. The code is available at https://github.com/HongtengXu/SGWB-Graphon.