For a new (forthcoming) research project, I have to brush up my knowledge about homology groups and triangulations. This constitutes an excellent opportunity to extend Aleph, my (also forthcoming) library for exploring persistent homology and its uses.

As a quick example, I decided to calculate some statistics about the homology of 2-manifolds. Thanks to the work of Frank H. Lutz, a list of triangulations with up to 9 vertices, as well as a list of triangulations with 10 vertices, exist. After adding support for the appropriate data format in Aleph, I was able to easily read a family of triangulations and subsequently process them. Even though Aleph’s main purpose is the calculation of persistent homology, ordinary homology calculations work just as well—ordinary homology being a special case of persistent homology.

As a starting point for further mathematical ruminations, let us take a look at the distribution of Betti numbers of 2-manifolds. More precisely, we analyse the distribution of the first Betti number of the manifold—according to PoincarĂ© duality, the zeroth and second Betti number have to equal, and we know the zeroth Betti number to be 1 for a manifold.

Without further ado, here is a tabulation of first Betti numbers for triangulations with up to 9 vertices:

Value Number of occurrences
0 73
1 154
2 313
3 133
4 37
5 2

The results are interesting: most triangulations appear to have the homology of a torus, i.e. a first Betti number of 2, followed by the homology of the real projective plane with a first Betti number of 1. Betti numbers larger than 3 are extremely rare. Intuitively, this makes sense—the triangulation only consists of at most 9 vertices, so there are natural limits on how high the Betti number can become.

For triangulations with 10 vertices, another distribution arises:

Value Number of occurrences
0 233
1 1210
2 6571
3 11784
4 14522
5 7050
6 1042
7 14

Here, the mode of Betti numbers is a value of 4, with 14522 occurrences out of 42426 triangulations. The homology type of this signature is the one of a genus-4 surface. Again, higher values get progressively less likely because only 10 vertices are permitted.

I am not yet sure what to make of this, but it sure is a nice test case and application scenario for Aleph. One of the next blog posts will give more details about this calculation, and its implementation using the library. Stay tuned!

Posted late Saturday night, July 30th, 2017 Tags:

In a previous article, I discussed the distribution of Betti numbers of triangulations of 2-manifolds. This article now discusses the code. Coincidentally, it also demonstrates how to use Aleph, my (forthcoming) library for exploring persistent homology and its uses.

Aleph is header-only, so it can be readily deployed in your own projects. In the following, I am assuming that you have installed Aleph, following the official instructions. There are several tutorials available that cover different parts of the persistent homology calculation process. For our purpose, viz. the calculation of homology of triangulated spaces, no large machinery is needed. We start with some includes and `using` directives:

```#include <aleph/topology/io/LexicographicTriangulation.hh>

#include <aleph/persistentHomology/Calculation.hh>

#include <aleph/topology/Simplex.hh>
#include <aleph/topology/SimplicialComplex.hh>

#include <iostream>
#include <string>
#include <vector>

using DataType          = bool;
using VertexType        = unsigned short;

using Simplex           = aleph::topology::Simplex<DataType, VertexType>;
using SimplicialComplex = aleph::topology::SimplicialComplex<Simplex>;
```

Nothing fancy happened so far. We set up a simplex class, which is the basic data type in most applications, as well as a simplicial complex, which is the basic storage class for multiple simplices. The only interesting thing is our choice of `DataType` and `VertexType`. Since our simplices need not store any additional data except for their vertices, we select `bool` as a data type in order to make the class smaller. This could potentially be achieved with EBCO as well, but I did not yet have time to test it adequately. In addition to the data type, we use `unsigned short` as the vertex type—the triangulations that we want to analyse only feature 9 or 10 vertices, so `unsigned short` is the best solution for storing vertex identifiers.

Next, we need some I/O code to read a lexicographic triangulation:

```int main(int argc, char* argv[])
{
if( argc <= 1 )
return -1;

std::string filename = argv[1];
std::vector<SimplicialComplex> simplicialComplexes;

aleph::topology::io::LexicographicTriangulationReader reader;
reader( filename, simplicialComplexes );

for( auto&& K : simplicialComplexes )
{
K.createMissingFaces();
K.sort();
}
}
```

Here, we used the `LexicographicTriangulationReader`, a reader class that supports reading files in the format defined by Frank H. Lutz. However, this format only provides the top-level simplices of a triangulation. Hence, for a 2-manifold, only the 2-simplices are specified. For calculating homology groups, however, all simplices are required. Luckily, the `SimplicialComplex` class offers a method for just this purpose. By calling `createMissingFaces()`, all missing faces of the simplicial complex are calculated and added to the simplicial complex. Afterwards, we use `sort()` to sort simplices in lexicographical order. This order is required to ensure that the homology groups can be calculated correctly—the calculation routines assume that the complex is being filtrated, so faces need to precede co-faces.

Having created and stored a list of simplicial complexes, we may now finally calculate their homology groups by adding the following code after the last `for`-loop:

```for( auto&& K : simplicialComplexes )
{
bool dualize                    = true;
bool includeAllUnpairedCreators = true;

auto diagrams
= aleph::calculatePersistenceDiagrams( K,
dualize,
includeAllUnpairedCreators );
}
```

This code calls `calculatePersistenceDiagrams()`, which is usually employed to calculate, well, a set of persistence diagrams. The two flags `dualize` and `includeAllUnpairedCreators` also deserve some explanation. The first flag only instructs the convenience function as to whether the boundary matrix that is required for (persistent) homology calculations should be dualized or not. Dualization was shown to result in faster computations, so of course we want to use it as well. The second parameter depends on our particular application scenario. Normally, the persistent homology calculation ignores all creator simplices in the top dimension. The reason for this is simple: if we expand a neighbourhood graph to a Vietoris–Rips complex, the top-level simplices are an artefact of the expansion process. Most of these simplices cannot be paired with higher-dimensional simplices, hence they will appear to create a large number of high-dimensional holes. The persistent homology calculation convenience function hence ignores those simplices for the creation of persistence diagrams by default. For our application, however, keeping those simplices is actually the desired behaviour—the top-level simplices are well-defined and an integral part of the triangulation.

As a last step, we want to print the signature of Betti numbers for the given simplicial complex. This is easily achieved by adding a nested loop at the end of the `for`-loop:

```for( auto&& diagram : diagrams )
{
auto d = diagram.dimension();
std::cout << "b_" << d << " = " << diagram.betti() << "\n";
}
```

This will give us all non-zero Betti numbers of the given triangulation. The output format is obviously not optimal—in a research setting, I like to use JSON for creating human-readable and machine-readable results. If you are interested in how this may look, please take a look at a more involved tool for dealing with triangulations. Be aware, however, that this tool is still a work in progress.

This concludes our little foray into Aleph. I hope that I piqued your interest! By the way, I am always looking for contributors…

Posted Sunday evening, July 30th, 2017