A Survey of Models and Algorithms for Social Influence Analysis

2011 ◽  
pp. 177-214 ◽  
Author(s):  
Jimeng Sun ◽  
Jie Tang
Keyword(s):  
Social Influence ◽  
Influence Analysis ◽  
Social Influence Analysis

Social Influence Analysis

2014 ◽  
pp. 1800-1807 ◽  
Author(s):  
Tiziana Guzzo ◽  
Fernando Ferri ◽  
Patrizia Grifoni
Keyword(s):  
Social Influence ◽  
Influence Analysis ◽  
Social Influence Analysis

Weapons of Influence Misused: A Social Influence Analysis of Why People Fall Prey to Internet Scams

2014 ◽  
Vol 8 (7) ◽  
pp. 388-396 ◽  
Author(s):  
Nicole L. Muscanell ◽  
Rosanna E. Guadagno ◽  
Shannon Murphy
Keyword(s):  
Social Influence ◽  
Influence Analysis ◽  
Social Influence Analysis ◽  
Internet Scams

User Profile Visualization to Facilitate MSLIM-Model-Based Social Influence Analysis Based on Slow Intelligence Approach

2014 ◽  
Vol 24 (10) ◽  
pp. 1573-1587
Author(s):  
Yingze Wang ◽  
Shi-Kuo Chang
Keyword(s):  
Social Influence ◽  
Information Diffusion ◽  
Model Performance ◽  
User Profile ◽  
Central Research ◽  
Active Node ◽  
Influence Analysis ◽  
Intelligence System ◽  
Influence Model ◽  
Social Influence Analysis

Identifying the hub nodes (the most influential users) in an information diffusion network is a central research topic in social influence analysis. In [2], we developed the Multi-task Sparse Linear Influence Model (MSLIM) to automatically select topic-sensitive influential nodes in an implicit network. Our method firstly needs to determine the active node set to model the global volume of the contagions of the entire network nodes. The active node set is chosen by a simple rule in [2]. However, different set leads to different model performance. Reference [4] proposed a new framework called Slow Intelligence System, which can continuously learn, search the appropriate method and propagate information according to the environment to improve the performance over time. In this paper, we develop a slow intelligence based active user definition system by utilizing user profile visualization to help interactively determine active node sets in MSLIM model. We apply our system on a set of 2.6 million tweets of 1000 users on twitter. We show that our system can efficiently utilize user's profile visualization techniques to facilitate determining the appropriate active node sets.

Sign in / Sign up

Export Citation Format

Share Document