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Real Time Social Network Data Analysis for Community Detection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Mohammad Sarwar Jahan Morshed, Akinul Islam Jony

Mohammad Sarwar Jahan Morshed and Akinul Islam Jony. Article: Real Time Social Network Data Analysis for Community Detection. International Journal of Computer Applications 139(6):1-5, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Mohammad Sarwar Jahan Morshed and Akinul Islam Jony},
	title = {Article: Real Time Social Network Data Analysis for Community Detection},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {6},
	pages = {1-5},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In WWW becomes a widely used platform for different social networks and social medias for the social communication. This platform becomes the oasis of a huge amount of data. Therefore, this data repository draws tremendous attention from corporate, government, NGOs, social workers, politician, etc. to either promote their products or to convey their message to the targeted community. But identification of community structure and social graph becomes a challenging issue for the social network researcher and graph theory researchers since the pervasive usage of instant messaging systems and fundamental shift in publishing contents in these social medias. Although a lot of attention has been given by the researcher to introduce several algorithms for identifying the community structure, most of them are not suitable for dealing with the large scale social network data in real time. This paper presents a model for community detection from social graph using the real time data analytic. In this paper, we introduce data analytic algorithms that can analysis contextual data. These algorithms can analyze large scale social interaction data and can detect a community based on the user supplied threshold value for community detection. Experiment result shows that the proposed algorithms can identify expected number meaningful communities from the social graph.


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Real time data, Social Network, Community Detection, Big data, Data Analytic.