Paper Info
Title
Persuasion driven influence propagation in social networks
Abstract
With the explosive growth of online social network services such as Facebook and Twitter, people now are providing ample opportunities to share information, ideas, and innovations among others. Studying social influence and information diffusion in online social networks can be extremely useful for various real-life applications, notably influencer marketing and viral marketing. Influence maximization problem, motivated by viral marketing applications, has received extensive attentions from social network analysis community in recent years. The goal of the problem is to find a small set of k seed nodes in a social network that maximize the influence spread under certain influence propagation models. Based on the widely adopted Independent Cascade model, a Persuasiveness Aware Cascade (PAC) model which considers social persuasion in influence propagation is proposed. In this model, the user-to-user influence probability is estimated by three types of social persuasion, namely, tie strength, peer conformity, and authority. Experiments conducted over real-world social networks suggest that the proposed model with the new social persuasion measures is more effective in describing real-world influence propagation than the well-studied propagation models for influence maximization.
Year
DOI
Venue
2014
10.1109/ASONAM.2014.6921640
ASONAM '14: Advances in Social Networks Analysis and Mining 2014 Beijing China August, 2014
Keywords
Field
DocType
probability,social networking (online),Facebook,Twitter,independent cascade model,influence maximization problem,influencer marketing,information diffusion,online social network services,persuasiveness aware cascade model,real-world influence propagation,social influence,social persuasion,user-to-user influence probability,viral marketing,influence maximization,propagation model,social persuasion
Data science,Social network,Persuasion,Computer science,Social influence,Artificial intelligence,Influencer marketing,World Wide Web,Viral marketing,Social network analysis,Conformity,Maximization,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4799-5876-4
3
0.38
References 
Authors
18
2
Name
Order
Citations
PageRank
Terrence Leung130.38
Fu-lai Chung224434.50