Maximizing Contrasting Opinions in Signed Social Networks
Maximizing Contrasting Opinions in Signed Social Networks A summary of the IEEE BigData 2019 research paper by Kaivalya Rawal and Arijit Khan. Background: A central characteristic of social networks is that it facilitates rapid dissemination of information among large groups of individuals [1]. Online social networks, such as Facebook, Twitter, LinkedIn, Flickr, and Digg are used for spreading ideas and messages. Users’ behaviors and opinions are highly affected by their friends in social networks, which is defined as the social influence. Motivated by various real-world applications, e.g., viral marketing [2], social and political campaigning [3], social influence studies have attracted extensive research attention. The classic influence maximization problem [4], [2] identifies the top-k seed users in a social network such that the expected number of influenced users in the network, starting from those seeds and following an influence diffusion model, is maximized. The