论文标题
神经二手匹配
Neural Bipartite Matching
论文作者
论文摘要
图神经网络（GNN）已找到在算法空间中学习的应用。但是，现有研究选择的算法（分类，最广泛的搜索，最短的路径查找等）通常与标准GNN体系结构完全吻合。本报告描述了如何将神经执行应用于复杂算法，例如通过将其减少到流量问题并使用福特·富尔克森（Ford-Fulkerson）找到最大流量来找到最大的两分匹配。这是通过仅基于由单个GNN生成的功能的神经执行来实现的。该评估显示了网络实现几乎100％的最佳匹配的强烈概括结果。
Graph neural networks (GNNs) have found application for learning in the space of algorithms. However, the algorithms chosen by existing research (sorting, Breadth-First search, shortest path finding, etc.) usually align perfectly with a standard GNN architecture. This report describes how neural execution is applied to a complex algorithm, such as finding maximum bipartite matching by reducing it to a flow problem and using Ford-Fulkerson to find the maximum flow. This is achieved via neural execution based only on features generated from a single GNN. The evaluation shows strongly generalising results with the network achieving optimal matching almost 100% of the time.
