An implementation of Newman's leading eigenvector algorithm for finding communities in data.
Development was done in WSL 2. VS Code launch configurations exist for the main application and all benchmark files. Running this (from the debug tab in VS Code) will rebuild and run the selected application.
Running the main application will performance community detection on the Zachary Karate Network and output the communities to a text file.
If you do not wish to use VS Code to build and run, the following compilation commands will be necessary:
Compile the libraries: cd ./lib && gcc -o CDUtils.o -c CDUtils.c -fopenmp -O3 -march=native
Compiling main: gcc -fopenmp -g main.c -o main ./lib/CDUtils.o -lm -Wall -Werror -Wpedantic -Waggressive-loop-optimizations -O3 -march=native
Run with ./main
The program will place the communities file in ./benchmarking/matlab_testing_files/matlab-communities.txt. The file should contain:
24 25 26 28 29 32
9 10 15 16 19 21 23 27 30 31 33 34
1 5 6 7 11 12 17
2 3 4 8 13 14 18 20 22
Each line is a community.
Random adjacency matrices can be generated. Open main.c and find: // uncomment if you wish to randomly generate a network to analyze
This section of code contains the code necessary to allocate space for an adjacency matrix and fill it (using genAdjacencyMatrix). Don't forget to change MATRIX_SIZE above main().