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.

翻译：图神经网络（GNNs）在算法空间的学习中已得到应用。然而，现有研究选择的算法（排序、广度优先搜索、最短路径查找等）通常与标准GNN架构完美契合。本报告描述了神经执行如何应用于复杂算法，例如通过将最大二分图匹配问题转化为流问题并使用Ford-Fulkerson算法求解最大流。这是通过仅基于单个GNN生成的特征进行神经执行来实现的。评估结果表明，该网络具有强大的泛化能力，几乎100%地实现了最优匹配。