Skip to main content
Article thumbnail
Location of Repository

Generalized Louvain Method for Community Detection in Large Networks

By Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara and Alessandro Provetti

Abstract

In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the k-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.\u

Topics: Artificial Intelligence, Dynamical Systems
Year: 2011
OAI identifier: oai:cogprints.org:7667
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://cogprints.org/7667/1/is... (external link)
  • http://cogprints.org/7667/ (external link)
  • Suggested articles

    Citations

    1. (2001). A faster algorithm for betweenness centrality,”
    2. (2009). Communities in networks,”
    3. (2010). Community detection in graphs,”
    4. (2002). Community structure in social and biological networks,”
    5. (2008). Fast unfolding of communities in large networks,”
    6. (2004). Finding and evaluating community structure in networks,” Physical Review E,
    7. (2002). New spectral methods for ratio cut partitioning and clustering,” Computer-Aided Design of Integrated Circuits and Systems,
    8. (2007). On finding graph clusterings with maximum modularity,”
    9. (2001). On Spectral Clustering: Analysis and an algorithm,”
    10. (1989). Towards efficient hierarchical designs by ratio cut partitioning,” in

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.