This work builds upon The Aegypti Algorithm.
The Triangle-Free problem is a fundamental decision problem in graph theory. Given an undirected graph, the problem asks whether it's possible to determine if the graph contains no triangles (cycles of length 3). In other words, it checks if there exists a configuration where no three vertices are connected by edges that form a closed triangle.
This problem is important for various reasons:
- Graph Analysis: It's a basic building block for more complex graph algorithms and has applications in social network analysis, web graph analysis, and other domains.
-
Computational Complexity: It serves as a benchmark problem in the study of efficient algorithms for graph properties. While the naive approach has a time complexity of
$O(n^3)$ , there are more efficient algorithms with subcubic complexity.
Understanding the Triangle-Free problem is essential for anyone working with graphs and graph algorithms.
Input: A Boolean Adjacency Matrix
Question: Does
Answer: True / False
| c1 | c2 | c3 | c4 | c5 | |
|---|---|---|---|---|---|
| r1 | 0 | 0 | 1 | 0 | 1 |
| r2 | 0 | 0 | 0 | 1 | 0 |
| r3 | 1 | 0 | 0 | 0 | 1 |
| r4 | 0 | 1 | 0 | 0 | 0 |
| r5 | 1 | 0 | 1 | 0 | 0 |
The input for undirected graph is typically provided in DIMACS format. In this way, the previous adjacency matrix is represented in a text file using the following string representation:
p edge 5 4
e 1 3
e 1 5
e 2 4
e 3 5
This represents a 5x5 matrix in DIMACS format such that each edge
e W V
where the fields W and V specify the endpoints of the edge while the lower-case character e signifies that this is an edge descriptor line.
Example Solution:
Triangle Found (1, 3, 5): In Rows 3 & 5 and Columns 1 & 3
pip install aegypti- Go to the package directory to use the benchmarks:
git clone https://github.com/frankvegadelgado/finlay.git
cd finlay- Execute the script:
triangle -i .\benchmarks\testMatrix1utilizing the triangle command provided by Aegypti's Library to execute the Boolean adjacency matrix finlay\benchmarks\testMatrix1. The file testMatrix1 represents the example described herein. We also support .xz, .lzma, .bz2, and .bzip2 compressed text files.
Smart Algorithm for testMatrix1: Triangle Found (1, 3, 5)
which implies that the Boolean adjacency matrix finlay\benchmarks\testMatrix1 contains a triangle combining the nodes (1, 3, 5).
The -a flag enables the discovery of all triangles within the graph.
Example:
triangle -i .\benchmarks\testMatrix2 -aOutput:
Smart Algorithm for testMatrix2: Triangles Found (1, 3, 9); (1, 2, 11); (1, 3, 4); (1, 2, 8); (1, 3, 11); (2, 4, 11); (3, 4, 11); (2, 4, 9); (1, 4, 11); (4, 5, 11); (1, 4, 9); (1, 2, 9); (3, 4, 9); (1, 2, 6); (4, 5, 9); (1, 2, 4)
When multiple triangles exist, the output provides a list of their vertices.
Similarly, the -c flag counts all triangles in the graph.
Example:
triangle -i .\benchmarks\testMatrix2 -cOutput:
Smart Algorithm for testMatrix2: Triangles Count 16
We employ the same algorithm used to solve the triangle-free problem.
To display the help message and available options, run the following command in your terminal:
triangle -hThis will output:
usage: triangle [-h] -i INPUTFILE [-a] [-b] [-c] [-v] [-l] [--version]
Solve the Triangle-Free Problem for an undirected graph encoded in DIMACS format.
options:
-h, --help show this help message and exit
-i INPUTFILE, --inputFile INPUTFILE
input file path
-a, --all identify all triangles
-b, --bruteForce compare with a brute-force approach using matrix multiplication
-c, --count count the total amount of triangles
-v, --verbose anable verbose output
-l, --log enable file logging
--version show program's version number and exitThis output describes all available options.
A command-line tool, test_triangle, has been developed for testing algorithms on randomly generated, large sparse matrices. It accepts the following options:
usage: test_triangle [-h] -d DIMENSION [-n NUM_TESTS] [-s SPARSITY] [-a] [-b] [-c] [-w] [-v] [-l] [--version]
The Finlay Testing Application using randomly generated, large sparse matrices.
options:
-h, --help show this help message and exit
-d DIMENSION, --dimension DIMENSION
an integer specifying the dimensions of the square matrices
-n NUM_TESTS, --num_tests NUM_TESTS
an integer specifying the number of tests to run
-s SPARSITY, --sparsity SPARSITY
sparsity of the matrices (0.0 for dense, close to 1.0 for very sparse)
-a, --all identify all triangles
-b, --bruteForce compare with a brute-force approach using matrix multiplication
-c, --count count the total amount of triangles
-w, --write write the generated random matrix to a file in the current directory
-v, --verbose anable verbose output
-l, --log enable file logging
--version show program's version number and exitThis tool is designed to benchmark algorithms for sparse matrix operations.
It generates random square matrices with configurable dimensions (-d), sparsity levels (-s), and number of tests (-n). While a comparison with a brute-force matrix multiplication approach is available, it's recommended to avoid this for large datasets due to performance limitations. Additionally, the generated matrix can be written to the current directory (-w), and verbose output or file logging can be enabled with the (-v) or (-l) flag, respectively, to record test results.
- Python code by Frank Vega.
- MIT.
