SIAM Review, Volume 65, Issue 4, Page 919-962, November 2023.
The metric dimension of a graph is the smallest number of nodes required to identify all other nodes uniquely based on shortest path distances. Applications of metric dimension include discovering the source of a spread in a network, canonically labeling graphs, and embedding symbolic data in low-dimensional Euclidean spaces. This survey gives a self-contained introduction to metric dimension and an overview of the quintessential results and applications. We discuss methods for approximating the metric dimension of general graphs, and specific bounds and asymptotic behavior for deterministic and random families of graphs. We conclude with related concepts and directions for future work.

中文翻译：

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了解离散空间中的情况：公制尺寸及其应用的调查

SIAM Review，第 65 卷，第 4 期，第 919-962 页，2023 年 11 月。
图的度量维度是基于最短路径距离唯一识别所有其他节点所需的最小节点数。度量维度的应用包括发现网络中的传播源、规范地标记图以及在低维欧几里得空间中嵌入符号数据。本调查对公制维度进行了独立的介绍，并对典型结果和应用进行了概述。我们讨论近似一般图的度量维度的方法，以及确定性和随机图族的特定边界和渐近行为。我们总结了相关概念和未来工作的方向。