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arXiv 2508.08025math.ATcs.CG
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Dowker 复形的旗化

Flagifying the Dowker Complex

Marius Huber, Patrick Schnider

AI总结:

本文提出 Dowker 复形的旗化版本 Dowker-Rips 复形,以降低拓扑数据分析的计算成本,证明其在 0、1 维同调上保持弱化对偶性并推广到持续同调,且在肿瘤微环境分类中实现加速而不损失性能。

摘要:

Dowker 复形 $\mathrm{D}_{R}(X,Y)$ 是一个单纯复形,刻画了两个有限集合 $X$ 和 $Y$ 在某个关系 $R\subseteq X\times Y$ 下的拓扑相互作用。虽然其定义是不对称的,但著名的 Dowker 对偶性表明 $\mathrm{D}_{R}(X,Y)$ 与 $\mathrm{D}_{R}(Y,X)$ 的几何实现具有同伦等价性。我们引入 Dowker-Rips 复形 $\mathrm{DR}_{R}(X,Y)$,其定义为 Dowker 复形的旗化,或等价地,定义为 $1$-骨架与 $\mathrm{D}_{R}(X,Y)$ 的 $1$-骨架一致的最大单纯复形。这一构造的动机来自拓扑数据分析中的应用,因为作为旗复形,Dowker-Rips 复形的计算成本低于 Dowker 复形。虽然 Dowker 对偶性一般对 Dowker-Rips 复形不成立,但我们证明仍有 $\mathrm{H}_{i}(\mathrm{DR}_{R}(X,Y))\cong\mathrm{H}_{i}(\mathrm{DR}_{R}(Y,X))$ 对 $i=0,1$ 成立。我们进一步证明这种弱化的对偶性可以推广到持续同调的情形,并通过交织(interleavings)来量化 Dowker 对偶性在同调维数高于 $1$ 时的“失效”程度。这使得 Dowker-Rips 复形成为 Dowker 复形的一个计算成本更低、可用于拓扑数据分析的近似版本。实际上,我们提供了 Dowker-Rips 复形的 Python 实现,并作为一个应用,展示了它可以在肿瘤微环境分类流程中作为 Dowker 复形的直接替代品。在该流程中,使用 Dowker-Rips 复形可以在保持分类性能的同时提高速度。

英文摘要:

The Dowker complex $\mathrm{D}_{R}(X,Y)$ is a simplicial complex capturing the topological interplay between two finite sets $X$ and $Y$ under some relation $R\subseteq X\times Y$. While its definition is asymmetric, the famous Dowker duality states that $\mathrm{D}_{R}(X,Y)$ and $\mathrm{D}_{R}(Y,X)$ have homotopy equivalent geometric realizations. We introduce the Dowker-Rips complex $\mathrm{DR}_{R}(X,Y)$, defined as the flagification of the Dowker complex or, equivalently, as the maximal simplicial complex whose $1$-skeleton coincides with that of $\mathrm{D}_{R}(X,Y)$. This is motivated by applications in topological data analysis, since as a flag complex, the Dowker-Rips complex is less expensive to compute than the Dowker complex. While the Dowker duality does not hold for Dowker-Rips complexes in general, we show that one still has that $\mathrm{H}_{i}(\mathrm{DR}_{R}(X,Y))\cong\mathrm{H}_{i}(\mathrm{DR}_{R}(Y,X))$ for $i=0,1$. We further show that this weakened duality extends to the setting of persistent homology, and quantify the ``failure" of the Dowker duality in homological dimensions higher than $1$ by means of interleavings. This makes the Dowker-Rips complex a less expensive, approximate version of the Dowker complex that is usable in topological data analysis. Indeed, we provide a Python implementation of the Dowker-Rips complex and, as an application, we show that it can be used as a drop-in replacement for the Dowker complex in a tumor microenvironment classification pipeline. In that pipeline, using the Dowker-Rips complex leads to increase in speed while retaining classification performance.

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