Paper Info
Title
Data-Driven Influence Learning In Social Networks
Abstract
How to model the influence diffusion process accurately is an open issue that it has attracted a lot of researchers in the field of social network analysis. The existing researches assume they have already owned the social graphs with edges labeled with the influence probability. However, the question of how to obtain these probability from social networks has been largely ignored. Thus, it is interesting to address the problem of how to model the influence diffusion based on the data of social graphs and action logs. This is the main problem we addressed in this paper, and our purpose is to solve the problem of seeds detection via the data-driven influence probability calculation. We consider the influence probability can be viewed as two parts of the influence strength and the influence threshold. For learning the influence probability, we propose a novel Data-driven Influence Learning (DIL) algorithm including three stages. The experimental results illustrate our algorithm performs better than other baselines in various datasets. In addition, our algorithm enables us to detect the seed sets in large social networks.
Year
DOI
Venue
2017
10.1109/ISPA/IUCC.2017.00177
2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017)
Keywords
Field
DocType
Influence learning, Online social networks, Data driven, Influence maximization
Data modeling,Diffusion process,Graph,Data-driven,Social network,Computer science,Social network analysis,Baseline (configuration management),Theoretical computer science,Human–computer interaction
Conference
ISSN
Citations 
PageRank 
2158-9178
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Feng Wang1102.83
Wenjun Jiang235624.25
Guojun Wang343747.52
Dongqing Xie427724.78