Abstract
Social networks are simply networks of social interactions and personal relationships. They have several properties, and community is one among them. These communities can be arranged by individuals in such a way that within the group they can connect more frequently compared to the outside of the group. Community detection can discover groups within a network where individuals’ group memberships are not explicitly given. These networks are represented in the form of graph. When graph size is increased then the number of communities will also be increased. Because of this complexity and dynamic nature of the graph, community detection in social network becomes a challenging task. Hence, more research is going on community detection, resulting in plenty of algorithms that come into picture to find effective way of detecting communities in a graph. In this paper, authors have presented different community detection algorithms and also discussed their pros and cons. Finally, authors stated some of the research challenges in this area.
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Chandusha, K., Chintalapudi, S.R., Krishna Prasad, M.H.M. (2019). An Empirical Study on Community Detection Algorithms. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_4
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