Review of Community Detection over Social Media:Graph Prospective

Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

@article{Jain2019,
  title     = {Review of Community Detection over Social Media:Graph Prospective},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {2},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Pranita Jain and Deepak Singh Tomar},
  doi       = {10.14569/IJACSA.2019.0100274},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100274}
}