用星系点云数据测试对称性保持的图神经网络性能
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
- 设计基于宇宙对称性的星系点云基准测试
- 对称性保持模型在多尺度信息捕捉上优于普通模型
- 现有架构仍难有效提取远距离相关性，适合研究对称学习者
高效处理结构化点云数据并保留多尺度信息是图形学与原子建模等领域的关键挑战。本文使用模拟星系位置和属性构成的点云数据集，评估图神经网络同时捕捉局部聚集环境与长程相关性的能力。鉴于宇宙的均匀各向同性特性，该数据具有高度对称性。因此重点评测了欧几里得对称性保持（E(3)-等变）图神经网络，结果表明其在下游任务表现及模拟效率上均优于非等变模型和领域特定方法。然而，当前架构在提取长程信息方面仍不及领域专用基线，提示未来需设计更优架构以提升长程感知能力。
原文摘要 · Abstract (English)
Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated dataset of simulated galaxy positions and properties, represented as point clouds, we benchmark the ability of graph neural networks to simultaneously capture local clustering environments and long-range correlations. Given the homogeneous and isotropic nature of the Universe, the data exhibits a high degree of symmetry. We therefore focus on evaluating the performance of Euclidean symmetry-preserving ($E(3)$-equivariant) graph neural networks, showing that they can outperform non-equivariant counterparts and domain-specific information extraction techniques in downstream performance as well as simulation-efficiency. However, we find that current architectures fail to capture information from long-range correlations as effectively as domain-specific baselines, motivating future work on architectures better suited for extracting long-range information.
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