A Parallel Community Detection Algorithm for Big Social Networks

Mining social networks has become an important task in data mining field, which describes users and their roles and relationships in social networks. Processing social networks with graph algorithms is the source for discovering many features. The most important algorithms applied to social networks are community detection algorithms. Communities of social networks are groups of people sharing common interests or activities. DenGraph is one of the density-based algorithms that used to find clusters of arbitrary shapes based on users’ interactions in social networks. However, because of the rapidly growing size of social networks, it is impossible to process a huge graph on a single machine in an acceptable level of execution. In this article, DenGraph algorithm has been redesigned to work in distributed computing environment. We proposed ParaDengraph Algorithm based on Pregel parallel model for large graph processing.

@article{AlQahtani2018,
  title     = {A Parallel Community Detection Algorithm for Big Social Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Yathrib AlQahtani and Mourad Ykhlef},
  doi       = {10.14569/IJACSA.2018.090146},
  url       = {https://doi.org/10.14569/IJACSA.2018.090146}
}