Many real-world data can be modeled as 3D graphs, but learning
representations that incorporates 3D information completely and efficiently is
challenging. Existing methods either use partial 3D information, or suffer from
excessive computational cost. To incorporate 3D information completely and
efficiently, we propose a novel message passing scheme that operates within
1-hop neighborhood. Our method guarantees full completeness of 3D information
on 3D graphs by achieving global and local completeness. Notably, we propose
the important rotation angles to fulfill global completeness. Additionally, we
show that our method is orders of magnitude faster than prior methods. We
provide rigorous proof of completeness and analysis of time complexity for our
methods. As molecules are in essence quantum systems, we build the
\underline{com}plete and \underline{e}fficient graph neural network (ComENet)
by combing quantum inspired basis functions and the proposed message passing
scheme. Experimental results demonstrate the capability and efficiency of
ComENet, especially on real-world datasets that are large in both numbers and
sizes of graphs. Our code is publicly available as part of the DIG library
(https://github.com/divelab/DIG).

本文提出了一种在三维图形中完全且高效地传递信息的新方案，通过使用重要的旋转角度实现了全局完整性，并组合了量子启发式基础函数和该方案提出的消息传递模式，构建了用于分子系统的全面高效图神经网络（ComENet）。