3-HBP: A Three-Level Hidden Bayesian Link Prediction Model in Social Networks
Abstract of A Three-Level Hidden Bayesian Link Prediction
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.
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.
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.
Conclusion
In this 3-HBP: A Three-Level Hidden Bayesian Link Prediction Model in Social Networks paper,
Combing internal and external factors, our model can effectively improve the performance of link prediction. This paper used Twitter data for the experiments.
The experimental results showed that our proposed model 3-HBP can improve the performance of link prediction by comparing with other prediction baseline methods.
Through the study of link prediction in social networks, we can predict links among the users effectively,
and it can provide favorable support for the study of the estimation of network-level statistics and the evolution mechanism of the networks.
In the future work, we mainly focus on the application of link prediction.
