A Simple Acceleration Method for the Louvain Algorithm

@article{Ozaki2016ASA,
  title={A Simple Acceleration Method for the Louvain Algorithm},
  author={Naoto Ozaki and Hiroshi Tezuka and Mary Inaba},
  journal={International Journal of Computer and Electrical Engineering},
  year={2016},
  volume={8},
  pages={207-218}
}
The Louvain algorithm is well known for its high speed for detecting community structure in networks. In this paper, first, we analyze the Louvain algorithm as the preliminary experiment to uncover the processes that cause wasted computational time and their characteristics. Then based on this, we propose the Louvain Prune algorithm. The experiments show that the Louvain Prune algorithm significantly reduces computational time by up to 90%, and retains almost the same quality as the original… 

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References

SHOWING 1-10 OF 15 REFERENCES

Improving the Louvain Algorithm for Community Detection with Modularity Maximization

TLDR
This paper presents an enhancement of the well-known Louvain algorithm for community detection with modularity maximization by including a uncoarsening phase, leading to a full multi-level method.

Faster unfolding of communities: speeding up the Louvain algorithm

  • V. Traag
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2015
TLDR
This work suggests to consider moving nodes to a random neighbor community, instead of the best neighborcommunity, which reduces the theoretical runtime complexity from O(m) to O(nlog〈k〉) in networks with a clear community structure.

A smart local moving algorithm for large-scale modularity-based community detection

TLDR
It is shown that the proposed smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular “Louvain algorithm”.

Fast unfolding of communities in large networks

TLDR
This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.

Accelerating Community Detection by Using K-core Subgraphs

TLDR
This work proposes a framework that accelerates community detection by applying an expensive algorithm (modularity optimization, the Louvain method, spectral clustering, etc.) to the K-core and then using an inexpensive heuristic to infer community labels for the remaining nodes.

Fast parallel algorithm for unfolding of communities in large graphs

TLDR
The proposed distributed memory parallel algorithm targets the costly first iteration of the initial method by parallelizing it and achieves up to ≈5× performance improvement as compared to the sequential version while not compromising the correctness of the final result.

An ensemble learning strategy for graph clustering

TLDR
It is shown, that the quality of the initial weak clusterings is of minor importance for the final result of the scheme if the authors iterate the process of restarting from maximal overlaps, and its search behavior is linked to global analysis.

Benchmark graphs for testing community detection algorithms.

TLDR
This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.

KONECT: the Koblenz network collection

TLDR
KONECT's taxonomy of networks datasets is described, an overview of the datasets included, a review of the supported statistics and plots, and the project's role in the area of web science and network science are discussed.

Resolution limit in community detection

TLDR
It is found that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined.