PROJECT TITLE :

Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis - 2018

ABSTRACT:

Data will be disseminated widely and rapidly through Online Social Networks (OSNs) with “word-of-mouth” effects. Viral promoting is such a typical application in which new product or business activities are advertised by some seed users in OSNs to different users in an exceedingly cascading manner. The selection of initial seed users yields a tradeoff between the expense and reward of viral marketing. In this Project, we define a general profit metric that naturally combines the advantage of influence unfold with the value of seed selection in viral marketing. We do a comprehensive study on finding a collection of seed nodes to maximise the profit of viral promoting. We have a tendency to show that the profit metric is considerably totally different from the influence metric in that it's no longer monotone. This characteristic differentiates the profit maximization drawback from the traditional influence maximization downside. We tend to develop new seed choice algorithms for profit maximization with robust approximation guarantees. We have a tendency to also derive several upper bounds to benchmark the practical performance of an algorithm on any specific problem instance. Experimental evaluations with real OSN datasets demonstrate the effectiveness of our algorithms and techniques.