Paper Info
Title
The Influence Maximization Problem in the Network Under Node Personalized Characteristics
Abstract
Considering the personalization of nodes in complex networks, we propose an algorithm that maximizes the influence of similarity based on overlapping nodes and nodes. In the proposed algorithm, we investigate the overlapping of communities in the network and use overlapped nodes as the initial propagation seed set. Then, we discuss the personalized characteristics of nodes and introduce the concept of sparse attributes. An improved independent cascaded model is built to integrate the similarity of node attributes. Finally, experiments are performed on real data sets. The results show that the proposed algorithm is better than others.
Year
DOI
Venue
2018
10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00046
2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
Keywords
Field
DocType
Influence maximization,independent cascade model,Overlapping nodes,Attribute similarity
Data mining,Data set,Computer science,Greedy algorithm,Complex network,Maximization,Personalization
Conference
ISBN
Citations 
PageRank 
978-1-5386-7519-9
0
0.34
References 
Authors
10
6
Name
Order
Citations
PageRank
Weimin Li16325.40
Jun Mo291.61
Yue Liu344184.32
N. Ito4456.08
Yohsuke Murase564.01
Jianwei Liu6710.27