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README.md

VieClus v1.0

The graph clustering framework VieClus -- Vienna Graph Clustering.

Graph clustering is the problem of detecting tightly connected regions of a graph. Depending on the task, knowledge about the structure of the graph can reveal information such as voter behavior, the formation of new trends, existing terrorist groups and recruitment or a natural partitioning of data records onto pages. Further application areas include the study of protein interaction, gene expression networks, fraud detection, program optimization and the spread of epidemics---possible applications are plentiful, as almost all systems containing interacting or coexisting entities can be modeled as a graph.

This is the release of our memetic algorithm, VieClus (Vienna Graph Clustering), to tackle the graph clustering problem. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. In our experimental evaluation, we show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation challenge under consideration in a small amount of time. In fact, for most of the small instances, we can improve the old benchmark result in less than a minute. Moreover, while the previous best result for different instances has been computed by a variety of solvers, our algorithm can now be used as a single tool to compute the result.

Main project site:

http://vieclus.taa.univie.ac.at

Installation Notes

Before you can start you need to install the following software packages:

Once you installed the packages, just type ./compile.sh. Once you did that you can try to run the following command:

mpirun -n 2 ./deploy/vieclus examples/astro-ph.graph --time_limit=60

For a description of the graph format please have a look into the manual.