The use of graph neural networks has produced significant advances in point
cloud problems, such as those found in high energy physics. The question of how
to produce a graph structure in these problems is usually treated as a matter
of heuristics, employing fully connected graphs or K-nearest neighbors. In this
work, we elevate this question to utmost importance as the Topology Problem. We
propose an attention mechanism that allows a graph to be constructed in a
learned space that handles geometrically the flow of relevance, providing one
solution to the Topology Problem. We test this architecture, called
GravNetNorm, on the task of top jet tagging, and show that it is competitive in
tagging accuracy, and uses far fewer computational resources than all other
comparable models.

使用图神经网络在高能物理等领域中解决点云问题取得了显著进展，本文将问题提升为拓扑问题，并提出了一种注意力机制，允许在学习空间中构建图结构以处理相关性流动，从而提供了一种拓扑问题的解决方案，我们在顶夸克标记任务上测试了名为 GravNetNorm 的这种架构，并显示其标记精度具有竞争力，且使用的计算资源远少于其他可比较的模型。