In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity in social network users. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, Bayesian network distributed storage and parallel reasoning algorithm is proposed based on Hadoop platform in this paper. Experimental results verify the efficiency and correctness of the algorithm.
A Method for Determining the Credibility of Intrusion Detection based on a Probabilistic Graph Model in the Proactive Protection Subsystem of the Operational Cybersecurity Center