论文标题
深图匹配共识
Deep Graph Matching Consensus
论文作者
论文摘要
这项工作提出了一个两阶段的神经体系结构，用于学习和完善图之间的结构对应关系。首先，我们使用图形神经网络计算的局部节点嵌入式来获取节点之间软对应关系的初始排名。其次，我们采用同步消息传递网络到迭代地重新排列软对应关系，以在图之间的本地社区中达成匹配的共识。我们从理论和经验上表明，我们的消息传递方案计算相应社区的共识衡量标准，然后将其用于指导迭代重新排列过程。我们纯粹的本地和稀疏感知体系结构可以很好地扩展到大型现实世界的输入，同时仍然能够始终如一地恢复全局对应关系。我们证明了我们的方法对来自计算机视觉和实体对齐领域的现实世界任务的实际有效性，而知识图之间的实体对齐方式，我们可以在当前的最新面前改进。我们的源代码可在https://github.com/rusty1s/ deep-graph-matching-consensus下获得。
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art. Our source code is available under https://github.com/rusty1s/ deep-graph-matching-consensus.
