Optimization Problems for Maximizing Influence in Social Networks

Social Networks have become very popular in the past decade. They started as platforms to stay connected with friends and family living in different parts of the world, but have evolved into so much more, resulting in Social Network Analysis (SNA) becoming a very popular area of research. One popular problem under the umbrella of SNA is Influence Maximization (IM), which aims at selecting k initially influenced nodes (users) in a social network that will maximize the expected number of eventually-influenced nodes (users) in the network. Influence maximization finds its application in many domains, such as viral marketing, content maximization, epidemic control, virus eradication, rumor control and misinformation blocking. In this dissertation, we study various variations of the IM problem such as Composed Influence Maximization, Group Influence Maximization, Profit Maximization in Groups and Rumor Blocking Problem in Social Networks. We formulate objective functions for these problems and as most of them are NP-hard, we focus on finding methods that ensure efficient estimation of these functions. The two main challenges we face are submodularity and scalibility. To design efficient algorithms, we perform simulations with sampling techniques to improve the effectiveness of our solution approach.