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https://github.com/uscbiostats/barry
barry: Your go-to motif accountant
https://github.com/uscbiostats/barry
change-statistics counting-statistics enumeration ergm exponential-family graph motif
Last synced: 5 days ago
JSON representation
barry: Your go-to motif accountant
- Host: GitHub
- URL: https://github.com/uscbiostats/barry
- Owner: USCbiostats
- License: mit
- Created: 2019-12-19T18:15:48.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-11-16T23:49:02.000Z (almost 1 year ago)
- Last Synced: 2023-11-17T00:55:36.500Z (almost 1 year ago)
- Topics: change-statistics, counting-statistics, enumeration, ergm, exponential-family, graph, motif
- Language: C++
- Homepage: https://USCbiostats.github.io/barry
- Size: 269 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
[](https://github.com/USCbiostats/barry/actions/workflows/c-cpp.yml)
[](https://USCbiostats.github.io/barry)
[](https://codecov.io/gh/USCbiostats/barry)
[](https://image.usc.edu)
Barry: your to-go motif accountant
[This repository](https://github.com/USCbiostats/barry) contains a C++ template library that essentially counts sufficient statistics on binary arrays. Its primary goal is to provide a general framework for building discrete exponential-family models. A particular example is Exponential Random Graph Models (ERGMs), but we can use `barry` to deal with non-square arrays.
Among the key features included in `barry`, we have:
* Sparse arrays.
* User-defined count statistics.
* User-defined constrain of the support set.
* Powerset generation of binary arrays.
* Discrete Exponential Family Models module (DEFMs).
* Pooled DEFMs.
To use barry, you can either download the entire repository or, since it is header-only, the single header version [`barry.hpp`](barry.hpp).
This library was created and maintained by [Dr. George G. Vega Yon](https://ggvy.cl) as part of
his doctoral dissertation ["Essays on Bioinformatics and Social Network Analysis: Statistical and Computational Methods for Complex Systems."](https://digitallibrary.usc.edu/asset-management/2A3BF1WAN5IH)
# Examples
## Counting statistics in a graph
In the following code we create an array of size 5x5 of class `Network`
(available in the namespace [netcounters](https://uscbiostats.github.io/barry/namespacebarry_1_1counters_1_1network.html)), add/remove ties, print the
graph, and count common statistics used in ERGMs:
```cpp
#include
#include
#include "../include/barry.hpp"
typedef std::vector< unsigned int > vuint;
int main() {
// Creating network of size six with five ties
netcounters::Network net(
6, 6,
{0, 0, 4, 4, 2, 0, 1},
{1, 2, 0, 2, 4, 0, 1}
);
// How does this looks like?
net.print("Current view");
// Adding extra ties
net += {1, 0};
net(2, 0) = true;
// And removing a couple
net(0, 0) = false;
net -= {1, 1};
net.print("New view");
// Initializing the data. The program deals with freing the memory
net.set_data(new netcounters::NetworkData, true);
// Creating counter object for the network and adding stats to count
netcounters::NetStatsCounter counter(&net);
netcounters::counter_edges(counter.counters);
netcounters::counter_ttriads(counter.counters);
netcounters::counter_isolates(counter.counters);
netcounters::counter_ctriads(counter.counters);
netcounters::counter_mutual(counter.counters);
// Counting and printing the results
std::vector< double > counts = counter.count_all();
std::cout <<
"Edges : " << counts[0] << std::endl <<
"Transitive triads : " << counts[1] << std::endl <<
"Isolates : " << counts[2] << std::endl <<
"C triads : " << counts[3] << std::endl <<
"Mutuals : " << counts[4] << std::endl;
return 0;
}
```
Compiling this program using g++
```bash
g++ -std=c++11 -Wall -pedantic 08-counts.cpp -o counts && ./counts
```
Yields the following output:
```bash
Current view
[ 0,] 1 1 1 . . .
[ 1,] . 1 . . . .
[ 2,] . . . . 1 .
[ 3,] . . . . . .
[ 4,] 1 . 1 . . .
[ 5,] . . . . . .
New view
[ 0,] . 1 1 . . .
[ 1,] 1 . . . . .
[ 2,] 1 . . . 1 .
[ 3,] . . . . . .
[ 4,] 1 . 1 . . .
[ 5,] . . . . . .
Edges : 7
Transitive triads : 3
Isolates : 2
C triads : 1
Mutuals : 3
```
# Features
## Efficient memory usage
One of the key features of `barry` is that it will handle memory efficiently. In the case of pooled-data models, the module for statistical models avoids double-counting support when possible by keeping track of what datasets (networks, for instance) share the same.
# Documentation
More information can be found in the Doxygen website [here](https://uscbiostats.github.io/barry) and in the PDF version
of the documentation [here](https://github.com/USCbiostats/barry/blob/gh-pages/latex/refman.pdf).
# Code of Conduct
Please note that the `barry` project is released with a
[Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.