Social Network User Influence Evaluation Model Based on Bayesian Derived Classifier
To prevent the rapid spread of negative information on social networks,influential social network nodes must be identified by evaluating the influence of users on social networks.To address the problem of missing cross-characteristics in traditional algorithms in the field of social networks,this study proposes a network user influence evaluation model combined with Gaussian Bayesian-derived classifiers.The model first combines user activity,contact,coverage,and other dimensions to establish social network user influence characterization indicators.Simultaneously,the relationship characteristics between social network users and user behavior characteristics are considered to reduce the impact of zombie fans and garbage social networks on network evaluation results.A model-solving method based on a Gaussian Bayesian classifier is proposed by establishing a continuous attribute-naive Bayes classifier method.The key factors affecting the evaluation model are analyzed in depth using 152 059 423 media and newspaper user comments from Sina Weibo as experimental data.A comparative experiment with traditional models,such as HRank,is conducted using simulation software to verify the feasibility of the model.The experimental results indicate that the model reflects the cross-characteristics of social network users and improves its practicality.Compared to traditional algorithms,this model tends to have more stable classification errors,lower error rates in the classification results,and better adaptability.
social networkinfluenceBayesian derived classifierevaluation modeluser activity
万方数据
维普
