Computing Zigzag Persistence on Graphs in Near-Linear Time
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Abstract (arXiv)
Graphs model real-world circumstances in many applications where they may constantly change to capture the dynamic behavior of the phenomena. Topological persistence which provides a set of birth and death pairs for the topological features is one instrument for analyzing such changing graph data. However, standard persistent homology defined over a growing space cannot always capture such a dynamic process unless shrinking with deletions is also allowed. Hence, zigzag persistence which incorporates both insertions and deletions of simplices is more appropriate in such a setting. Unlike standard persistence which admits nearly linear-time algorithms for graphs, such results for the zigzag version improving the general O(mω) time complexity are not known, where ω< 2.37286 is the matrix multiplication exponent. In this paper, we propose algorithms for zigzag persistence on graphs which run in near-linear time. Specifically, given a filtration with m additions and deletions on a graph with n vertices and edges, the algorithm for 0-dimension runs in O(mlog2 n+mlog m) time and the algorithm for 1-dimension runs in O(mlog4 n) time. The algorithm for 0-dimension draws upon another algorithm designed originally for pairing critical points of Morse functions on 2-manifolds. The algorithm for 1-dimension pairs a negative edge with the earliest positive edge so that a 1-cycle containing both edges resides in all intermediate graphs. Both algorithms achieve the claimed time complexity via dynamic graph data structures proposed by Holm et al. In the end, using Alexander duality, we extend the algorithm for 0-dimension to compute the (p-1)-dimensional zigzag persistence for ℝp-embedded complexes in O(mlog2 n+mlog m+nlog n) time.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2021.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2026HalluZig: Hallucination Detection using Zigzag Persistence4 Jan 2026
- 2025TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction31 May 2025
- 2023Algebraic and Geometric Models for Space Networking3 Apr 2023
- 2023On the Expressivity of Persistent Homology in Graph Learning20 Feb 2023