Measuring Homophily in Social Network: Identification of Flow of Inspiring Influence under New Vistas of Evolutionary Dynamics

Interaction with different person leads to different kinds of ideas and sharing or some nourishing effects which might influence others to believe or trust or even join some association and subsequently become the member of that community. This will facilitate to enjoy all kinds of social privileges. These concepts of grouping similar objects can be experienced as well as could be implemented on any Social Networks. The concept of homophily could assist to design the affiliation graph (of similar and close similar entities) of every member of any social network thus identifying the most popular community. In this paper we propose and discuss three tier data-mining algorithms) of a social network and evolutionary dynamics from graph properties perspective (embeddedness, betweenness and graph occupancy). A novel contribution is made in the proposal incorporating the principle of evolutionary dynamics to investigate the graph properties. The work also has been extended towards certain specific introspection about the distribution of the impact, and incentives of evolutionary algorithm for social network based events. The experiments demonstrate the interplay between on-line strategies and social network occupancy to maximize their individual profit levels.

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