Omkar2118/datasets huggingface dataset:
A collection of LFR benchmark graphs

This dataset is a collection of undirected and unweighted LFR benchmark graphs as proposed by Lancichinetti et al. [1]. We generated the graphs using the code provided by Santo Fortunato on his personal website [2], embedded in our evaluation framework [3], with two different parameter sets. Let N denote the number of vertices in the network, then

      Maximum community size: 0.2N (Set A); 0.1N (Set B)
      Minimum community size: 0.05N (Set A); 10 (Set B)
      Maximum node degree: 0.19N (Set A); 0.19N (Set B)
      Community size distribution exponent: 1.0 (Set A); 1.0 (Set B)
      Degree distribution exponent: 2.0 (Set A); 2.0 (Set B).

All other parameters assume default values. We provide graphs with different combinations of average degree, network size and mixing parameter for the given parameter sets:

    Set A: For average degrees in {15, 25, 50} we provide network sizes in {300, 600, 1200}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs.
    Set A: For average degrees in {15, 25, 50} we provide mixing parameters in {0.35, 0.45, 0.55}, each with network sizes in {300, 450, 600, 900, 1200, 1800, 2400, 3600, 4800, 6200, 9600, 19200}. For each configuration we provide 50 benchmark graphs.
    Set B: For average degrees in {20} we provide network sizes in {300, 600, 1200, 2400}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs.

Benchmark graphs are given in edge list format. Further, for each benchmark graph we provide ground truth communities as membership list and as structured datatype (.json), its generating random seeds and basic network statistics.

[1] Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical Review E 78(4):046110,https://doi.org/10.1103/PhysRevE.78.046110

[2] https://www.santofortunato.net/resources, Accessed: 19 Jan 2021

[3] https://github.com/synwalk/synwalk-analysis, Accessed: 19 Jan 2021

Introducing paper: Synwalk -- Community Detection via Random Walk Modelling