ABSTRACT:
In social networks, link establishment among the users is affected by
complex factors. In this paper, we try to investigate the internal and external
factors that affect the formation of links and propose a three-level hidden
Bayesian link prediction model by integrating the user behavior
as well as user relationships to link prediction. First, based on the user
multiple interest characteristics, a latent Dirichlet
allocation (LDA) traditional text modeling method is
applied into user behavior modeling.
Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent interest
distribution and analyze the effects of internal driving factors. Second, owing
to the power-law characteristics of user behavior, LDA
is improved by Gaussian weighting. In this way, the negative impact of the
interest distribution to the high-frequency users can be reduced and the
expression ability of interests can be enhanced. Furthermore, taking the impact
of common neighbor dependencies in link
establishment, the model can be extended with hidden naive Bayesian algorithm.
By quantifying the dependencies between common neighbors,
we can analyze the effects of external driving factors and combine internal
driving factors to link prediction. Experimental results indicate that the
model can not only mine user latent interest distribution but also can improve
the performance of link prediction effectively.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor :
15’’ LED
·
Input
Devices : Keyboard, Mouse
·
Ram : 1
GB
SOFTWARE
REQUIREMENTS:
·
Operating
system : Windows 7.
·
Coding
Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database
: MYSQL
REFERENCE:
Yunpeng Xiao , Xixi Li, Haohan Wang, Ming Xu, and Yanbing Liu, “3-HBP: A
Three-Level Hidden Bayesian Link Prediction Model in Social Networks”, IEEE
TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2018.