Seed Community Identification Framework for Community Detection over Social Media.

Abstract

The social media podium offers a communal perspective platform for web marketing, advertisement, political campaign, etc. It structures like-minded end-users over the explicit group as a community. Community structure over social media is the collaborative group of globally spread users having similar interests regarding a communal topic, product or any other axis. In recent years, researchers have widely used clustering techniques of data mining to structure communities over social media. Still, due to a lack of network and implicit communal information, researchers cannot bind mutually robust and modular community structures. The collaborative features of social media are inherent with implicit and explicit end-users. The explicit nature of both active and passive users is easily extracted from the graphical structure of social media. On the other hand, the degree of information inclusion of implicit features depends upon end-users participation. The Implicit features of frequently active users are diversely available, while integrating passive and silent users’ implicit features over the community is tedious. This work proposed a social theory based influence maximization (STIM) framework for community detection over social media. It combines user-generated content with profile information, extracts passive social media users through influence maximization, and provides the user space for influencing inactive users. The STIM framework clusters identical nodes over the maximum influencing node axis based on their graphical parameters such as node degree, node similarity, node reachability, modularity, and node density. This framework also provides the structural, relational and mathematical concept for the functional grouping of like-minded people as a community over social media through social theory. Finally, an evaluation has been carried out over six real-time datasets. It analyses that convolution neural network over STIM structure more dense and modular communities via influence maximization. STIM acquired around 93% modularity and 94% Normalized Mutual Information (NMI), resulting in approximately 2.23% and 5.69% improvements in modularity and NMI, respectively, over the best-acquired result of the benchmark approach.