The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain, to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code is available here: https://github.com/hangligit/pathfinding

翻译：人类大脑可以被视为由通过突触连接的数千亿生物神经元组成的图形结构。 它具有在出现某些神经元受损的情况下自动改变信息通过不同路径的路径的路径流动的非凡能力。 此外, 大脑能够保留信息并将其应用到类似但完全看不见的情景中。 在本文中, 我们从大脑的这些属性中汲取灵感, 开发一个计算框架, 以找到源节点与目标节点之间的最佳低成本路径。 我们显示我们的框架能够在测试时处理看不见的图形。 此外, 当节点被任意添加或在推断过程中被删除时, 它可以找到替代的最佳路径, 同时保持固定的预测时间 。 代码在这里可以找到 : https://github.com/ hangligiit/pathbound 。