Abstract
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.
Highlights
The Emergence of Social networking Site (SNS) like Facebook, Twitter, LinkedIn, MySpace, etc. open a new perspective for sharing, discussing, organizing and finding the information, experiences, contacts and contents
The propensity of end user towards specific tastes, preferences, and inclination to get associated in a social network leads to the formation of friend and community recommendation system to enhance web life
Community detection over SNS can be beneficial for locating a common research area in collaboration networks for traffic management [1], finding a set of likeminded users for profile Investigation [2], [3], marketing [4], [5], recommendations system [6], [7], political belonging [8], and detecting spammers on social networks [9]
Summary
The Emergence of Social networking Site (SNS) like Facebook, Twitter, LinkedIn, MySpace, etc. open a new perspective for sharing, discussing, organizing and finding the information, experiences, contacts and contents. Community detection over SNS can be beneficial for locating a common research area in collaboration networks for traffic management [1], finding a set of likeminded users for profile Investigation [2], [3], marketing [4], [5], recommendations system [6], [7], political belonging [8], and detecting spammers on social networks [9]. The rest of the paper is organized as follows: Section II presents overview of social media and their data inconsistency problem for community detection; Section III covers social media mining procedure for community detection and III(A)-III(C) explain social theory for deanonymized social relationship between social atom in social media data set.
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