Author:
Forouzandeh Saman,Sheikhahmadi Amir,Rezaei Aghdam Atae,Xu Shuxiang
Abstract
Purpose
This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks.
Design/methodology/approach
In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network.
Findings
Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media.
Originality/value
The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.
Subject
Computer Networks and Communications,Information Systems
Reference51 articles.
1. A survey of models and algorithms for social influence analysis,2011
2. Marketing based on user behavior in Facebook social network through recommender system design,2015
3. A learning-based model for predicting information diffusion in social networks: case of Twitter,2016
4. Betweenness centrality updation and community detection in streaming graphs using incremental algorithm,2017
Cited by
28 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献