Date of Award

2012

Degree Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Dr. Christie I. Ezeife

Keywords

Data Mining, Influence Maximization, Social Network, Trust Network

Rights

CC-BY-NC-ND 4.0

Abstract

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%.

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