Post #23

Recent papers on graph matching. Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching (NeurIPS 2019) https://nips.cc/Conferences/2019/Schedule?showEvent=13486 KerGM: Kernelized Graph Matching (NeurIPS 2019)https://nips.cc/Conferences/2019/Schedule?showEvent=14512 (Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs (NeurIPS 2019)https://nips.cc/Conferences/2019/Schedule?showEvent=13959 Gromov-Wasserstein Learning for Graph Matching and Node Embedding (ICML 2019)https://icml.cc/Conferences/2019/Schedule?showEvent=3845 Graph Matching Networks for Learning the Similarity of Graph Structured Objects (ICML 2019)https://deepmind.com/research/publications/Graph-matching-networks-for-learning-the-similarity-of-graph-structured-objects Learning deep graph matching with channel-independent embedding and Hungarian attention (ICLR 2020) https://openreview.net/forum?id=rJgBd2NYPH Deep Graph Matching Consensus (ICLR 2020) https://openreview.net/forum?id=HyeJf1HKvS Spectral Graph Matching and Regularized Quadratic Relaxations II: Erdős-Rényi Graphs and Universality (ICML 2020) https://arxiv.org/abs/1907.08883 Graph Optimal Transport for Cross-Domain Alignment (ICML 2020) https://arxiv.org/abs/2006.14744