Higher-order Persistence Diagrams
Abstract
Persistent homology tracks when topological features appear and disappear across filtered data and represents the lifetime of each feature by a persistence interval. In multiple instance learning, when the instances in a bag use filtrations over the same ordered parameter space, we can compare relationships between persistence intervals across instances. No existing method is designed to aggregate collections of persistence diagrams in multiple instance learning into a single persistence diagram of implications between intervals. We introduce higher-order persistence diagrams to represent relationships between persistence intervals and recursively construct each higher-order persistence interval from a pair of lower-order persistence intervals. We then aggregate these relationships across the persistence diagrams in a bag. Explicit construction of this aggregate is computationally expensive, so we introduce harmonic aggregation to compute coordinates of the aggregate from the lower-order persistence diagrams without explicitly constructing it. Computing a harmonic aggregation coordinate has lower asymptotic computational complexity than explicitly constructing the aggregate. We evaluate harmonic aggregation against persistence landscapes, persistence images, and PersLay on random network model and real-world graph-classification benchmarks, and find that harmonic aggregation has the highest accuracy on the random network model benchmark and on most real-world data benchmarks. Source code is available at https://github.com/cfanning8/higher-order-PDs.
Related
- Persistence diagrams of finite topological spaces
- A result of continuous stability for generalized persistence diagrams
- On the Spectral Synthesis of Lipschitz Vectorizations of Persistence Diagrams
- Statistical Inference for Persistence Diagrams via Landmark Embeddings: Minimax Theory and Finite Approximation
- Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget
BibTeX
@misc{fanning2026higherorder,
title = {{Higher-order Persistence Diagrams}},
author = {Charles Fanning and Mehmet Aktas},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.09866},
eprint = {2605.09866},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.09866},
}
RIS
TY - UNPB
TI - Higher-order Persistence Diagrams
AU - Charles Fanning
AU - Mehmet Aktas
PY - 2026
DA - 2026/10/25
PB - arXiv
DO - 10.48550/arxiv.2605.09866
UR - https://arxiv.org/abs/2605.09866
AB - Persistent homology tracks when topological features appear and disappear across filtered data and represents the lifetime of each feature by a persistence interval. In multiple instance learning, when the instances in a bag use filtrations over the same ordered parameter space, we can compare relationships between persistence intervals across instances. No existing method is designed to aggregate collections of persistence diagrams in multiple instance learning into a single persistence diagram of implications between intervals. We introduce higher-order persistence diagrams to represent relationships between persistence intervals and recursively construct each higher-order persistence interval from a pair of lower-order persistence intervals. We then aggregate these relationships across the persistence diagrams in a bag. Explicit construction of this aggregate is computationally expensive, so we introduce harmonic aggregation to compute coordinates of the aggregate from the lower-order persistence diagrams without explicitly constructing it. Computing a harmonic aggregation coordinate has lower asymptotic computational complexity than explicitly constructing the aggregate. We evaluate harmonic aggregation against persistence landscapes, persistence images, and PersLay on random network model and real-world graph-classification benchmarks, and find that harmonic aggregation has the highest accuracy on the random network model benchmark and on most real-world data benchmarks. Source code is available at https://github.com/cfanning8/higher-order-PDs.
ER -