From Louvain to Leiden: guaranteeing well-connected communities

被引:2198
作者
Traag, V. A. [1 ]
Waltman, L. [1 ]
van Eck, N. J. [1 ]
机构
[1] Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands
关键词
D O I
10.1038/s41598-019-41695-z
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.
引用
收藏
页数:12
相关论文
共 25 条
  • [1] [Anonymous], THESIS
  • [2] Scalable and Efficient Flow-Based Community Detection for Large-Scale Graph Analysis
    Bae, Seung-Hee
    Halperin, Daniel
    West, Jevin D.
    Rosvall, Martin
    Howe, Bill
    [J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2017, 11 (03)
  • [3] Fast unfolding of communities in large networks
    Blondel, Vincent D.
    Guillaume, Jean-Loup
    Lambiotte, Renaud
    Lefebvre, Etienne
    [J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2008,
  • [4] On modularity clustering
    Brandes, Ulrik
    Delling, Daniel
    Gaertler, Marco
    Goerke, Robert
    Hoefer, Martin
    Nikoloski, Zoran
    Wagner, Dorothea
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2008, 20 (02) : 172 - 188
  • [5] Complex brain networks: graph theoretical analysis of structural and functional systems
    Bullmore, Edward T.
    Sporns, Olaf
    [J]. NATURE REVIEWS NEUROSCIENCE, 2009, 10 (03) : 186 - 198
  • [6] Clauset A, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.066111
  • [7] Community detection in complex networks using extremal optimization
    Duch, J
    Arenas, A
    [J]. PHYSICAL REVIEW E, 2005, 72 (02)
  • [8] Resolution limit in community detection
    Fortunato, Santo
    Barthelemy, Marc
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2007, 104 (01) : 36 - 41
  • [9] Community detection in graphs
    Fortunato, Santo
    [J]. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2010, 486 (3-5): : 75 - 174
  • [10] Performance of modularity maximization in practical contexts
    Good, Benjamin H.
    de Montjoye, Yves-Alexandre
    Clauset, Aaron
    [J]. PHYSICAL REVIEW E, 2010, 81 (04)