A survey on community detection algorithms in large scale real world networks

Community Structure is one of the most relevant features of real world networks. Detecting such structures in large scale networks is a challenging task in scientific world. These are similar to clusters in which intra cluster density is more than the inter cluster density. This paper reviews the prominent community detection algorithms that detect both disjoint and overlapped communities. These algorithms are experimented on benchmark dataset Zachary's karate club. Obtained number of communities is compared with the ground truth. The quality measures namely modularity and Normalized Mutual Information (NMI) are computed for all disjoint community detection algorithms. As a result of voluminous research done in this area the overlapped communities are come into the picture. Overlapped community means that a node in the network may be affiliated to more than one community. To test these algorithms Omega index is also included in this survey. After reviewing all these algorithms, this survey concludes that quality and scalability are the major issues in this area and also the measure used for detecting communities needs more computational power. So, one need to use either high performance computing framework with Graphical Processing Units (GPU) or Hadoop framework for distributed computing. Hence, this will balance the trade-off between running time and quality.