论文标题
培训免费图形神经网络以匹配图
Training Free Graph Neural Networks for Graph Matching
论文作者
论文摘要
我们提出了一个训练免费图形匹配（TFGM）的框架，以提高基于图形神经网络（GNNS）的图形匹配的性能，从而在没有培训的情况下提供了快速有希望的解决方案（无培训）。 TFGM提供了四个广泛适用的原则，用于设计无培训的GNN，并且可以推广到受监督，半监督和无监督的图形匹配。这些钥匙是要将匹配的先验用来用培训来学习的匹配先验，并将其在GNN的建筑中学习，并在无训练环境下丢弃组件。进一步的分析表明，TFGM是图形匹配的二次分配公式的线性松弛，并将TFGM推广到一组广泛的GNN。广泛的实验表明，具有TFGM的GNN具有与完全训练的对应物相当（如果不是更好）的性能，并在无监督的环境中展示了TFGM的优势。我们的代码可在https://github.com/acharkq/training-free-graph-matching上找到。
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free). TFGM provides four widely applicable principles for designing training-free GNNs and is generalizable to supervised, semi-supervised, and unsupervised graph matching. The keys are to handcraft the matching priors, which used to be learned by training, into GNN's architecture and discard the components inessential under the training-free setting. Further analysis shows that TFGM is a linear relaxation to the quadratic assignment formulation of graph matching and generalizes TFGM to a broad set of GNNs. Extensive experiments show that GNNs with TFGM achieve comparable (if not better) performances to their fully trained counterparts, and demonstrate TFGM's superiority in the unsupervised setting. Our code is available at https://github.com/acharkq/Training-Free-Graph-Matching.
