Query Approach to Social Influence Maximization using Greedy and Dynamic Programming

Abstract

Influence maximization is familiarized to exploit the profit of viral marketing in social networks and their application. Impact amplification is used to augment the advantage of viral endorsing in informal organizations. The deficiency of impact growth is that it doesn't identify particular clients from others, regardless of the options that a few things can be helpful for the specific clients. For such things, it is a superior system to concentrate on boosting the impact on the specific clients. In this paper, we detail an impact boost issue as question handling to recognize particular client’s from others.  We express an influence maximization problem as query processing to differentiate exact users from others. We propose a model for the value of the objective function and a fast greedy-based approximation technique using the expectation model. For the expectation model, we examine a relationship of paths between clients. We propose a desire model for the estimation of the target volume and a quick envious based close estimation approach utilizing the desire model. For the desire model, we explore a relationship of ways between users. For the covetous technique, we determine a productive incremental overhauling of the negligible addition to our goal capacity.Keywords influence maximization, independent cascade model, social networks

References

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Published
2017-02-13
How to Cite
MARATHE, Rajdeep; KAPSE, Swati. Query Approach to Social Influence Maximization using Greedy and Dynamic Programming. International Journal Of Emerging Technology and Computer Science, [S.l.], v. 2, n. 1, feb. 2017. ISSN 2455-9954. Available at: <https://aspirepublishers.com/index.php/ijetcs/article/view/84>. Date accessed: 28 may 2020.

Keywords

nfluence maximization, independent cascade model, social networks