Benchmark¶
We suggested in the paper that more than half of the (realizable) degree-size sequences falls in the “polynomial regime”. How does this hold in real-world data? The answer is likely affirmative.
Note
We use \(\tau_{\text{c}} = \tau_{\text{r}} + \tau_{\text{b}}\) to denote the time that is necessary to determine if the input integer sequences can be realized as a simplicial complex. We call a joint sequence polynomial if \(\tau_{\text{c}} = 0\) (if simplicial) or \(\tau_{\text{c}} = 1\) (if not simplicial).
In that case, no backtracking (\(\tau_{\text{b}} = 0\)) is necessary, the algorithm either finds an instance in linear time or rejects simpliciality immediately.
The following results are measured with simplicial-test v1.2
, using 1 thread,
for a machine with Intel(R) Core(TM) i7-8700B CPU @ 3.20GHz (6 cores & 12 threads) and 64GB 2667MHz DDR4 memory.
TABLE—Summary of the joint degree sequences derived from empirical datasets. Shown are the convergence time \(\tau_\text{c}\), wall time, instance size \(E\), number of nodes (resp. facets) \(n\) (resp. \(m\)), maximal/minimal facet size, and maximal/minimal node degree.
Dataset |
\(\tau_\text{c}\) |
Wall time |
\(E\) |
\(m\) |
\(n\) |
\((\textbf{s}_\text{max}, \textbf{s}_\text{min})\) |
\((\textbf{d}_\text{max}, \textbf{d}_\text{min})\) |
Reference |
---|---|---|---|---|---|---|---|---|
crime |
0 |
621 ms |
805 |
194 |
551 |
(25, 1) |
(14, 1) |
|
flower and pollinator |
0 |
295 ms |
1206 |
91 |
679 |
(189, 1) |
(25, 1) |
|
human diseasome |
0 |
2.1 s |
1416 |
752 |
1100 |
(10, 1) |
(15, 1) |
|
senate committees |
0 |
1.57 s |
5143 |
275 |
282 |
(31, 4) |
(59, 1) |
|
house committees |
0 |
6.53 s |
11640 |
302 |
1290 |
(82, 3) |
(43, 1) |
|
contact high school |
3 |
1min 59s [*] |
11770 |
4862 |
327 |
(5, 2) |
(90, 2) |
|
contact primary school |
DNF |
N/A |
20615 |
8010 |
242 |
(5, 2) |
(174, 20) |
|
senate bills |
DNF |
N/A |
92876 |
3599 |
294 |
(99, 3) |
(1147, 1) |
|
mathoverflow answers |
0 |
6h 15min [*] |
131406 |
5296 |
73851 |
(1784, 2) |
(169, 1) |
|
walmart trips |
DNF |
N/A |
447347 |
63687 |
88860 |
(25, 2) |
(5412, 1) |
|
house bills |
848 |
52h 40min |
927075 |
23267 |
1494 |
(399, 2) |
(3824, 1) |
[*] We also run on a Raspberry Pi 4 (8GB) for the “contact high school” dataset, the wall time is 8min 36s. However, for the “mathoverflow answers” dataset, MemoryError is raised (it has a memory use peak at around 32GB).
DNF: Did Not Finish with the default cutoff at \(\tau_\text{c} = 10^5\).
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Charles Stewart III and Jonathan Woon. “Congressional Committee Assignments, 103rd to 114th Congresses, 1993–2017: House.”
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Rossana Mastrandrea, Julie Fournet, and Alain Barrat, “Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys.” PLOS ONE, 2015. DOI: 10.1371/journal.pone.0136497 [sci-hub], arXiv: 1506.03645.
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Philip S. Chodrow, Nate Veldt, and Austin R. Benson, “Hypergraph clustering: from blockmodels to modularity.” 2021. arXiv: 2101.09611.
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Nate Veldt, Austin R. Benson, and Jon Kleinberg. “Minimizing Localized Ratio Cut Objectives in Hypergraphs.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020. DOI: 10.1145/3394486.3403222 [sci-hub], arXiv: 2002.09441.
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Ilya Amburg, Nate Veldt, and Austin R. Benson. “Clustering in graphs and hypergraphs with categorical edge labels.” Proceedings of the Web Conference (WWW), 2020. DOI: 10.1145/3366423.3380152 [sci-hub], arXiv: 1910.09943.