Projects
Targeted Influence Maximization
In social networks, it is often the case that we need to spread ideas and influence through the network. For this to succeed, individual nodes with high influence should be identified so as to maximize the influence spread. This is the Influence Maximization problem. In Targeted Influence Maximization, we want to find influential nodes that maximize the influence to one set of “Target” users, while maintaining the number of “Non Targets” influenced below a threshold number. This work has appeared in the 2018 IEEE International Conference on Big Data.
Quantifying Attitude
Social networks act as a platform where users share ideas on a large scale. When users are exposed to an idea/concept, they can develop a strong attitude or weak attitude towards the idea/concept. Thus, due to repeated exposure to certain information, users can be influenced to strongly believe in a particular idea/message. In this project, we introduce a novel model to quantify Attitude of users in Social Networks. We pose practical optimization problems under this model and develop efficient scalable algorithms to solve them. This work has appeared in the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).