Date of Award
Dr. Christie I. Ezeife
Data Mining, Influence Maximization, Social Network, Trust Network
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
The goal of viral marketing is that, by the virtue of mouth to mouth word spread, a small set of influential customers can influence greater number of customers. Influence maximization (IM) task is to discover such influential nodes (or customers) from a social network. Existing algorithms adopt Greedy based approaches, which assume only positive influence among users. But in real life network, such as trust network, one can also get negatively influenced. In this research we propose a model, called T-GT model, considering both positive and negative influence. To solve IM under this model, a trust network where relationships among users are either `trust' or `distrust' is considered. We first compute positive and negative influence by mining frequent patterns of actions performed. Then using local search a new algorithm, called MineSeedLS, is proposed. Experimental results on real trust network shows that our approach outperforms Greedy based approach by almost 35%.
Ahmed, Sabbir, "Discovering Influential Nodes from Social Trust Network" (2012). Electronic Theses and Dissertations. 5407.