Our smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. See Links section for more information.
The output network will structurally be the same as the input network, but the nodes will be annotated with new attributes labeled "community_level_x", where x is a community level. The value of each of these attributes is the id of a community.
A single network is expected as the input, and a single network is produced as the output.
To integrate this algorithm in CIShell, a custom (Java) converter is used to convert the input network file to a edge list file that is proprietary to the compiled algorithm. The compiled algorithm is then executed upon this proprietary edge list file. The output community file is merged with the input network to produce the output network with annotations.
The output of this algorithm can be visualized well with the Circular Hierarchy visualization or using Gephi.
- Ludo Waltman, Nees Jan van Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection". Eur. Phys. J. B (2013) 86: 471.