基于多类特征的社交网络影响力预测研究综述OA

[Objective]Influence prediction,as an important content of social network analysis,has impor-tant social value and practical significance in many fields such as public opinion monitoring,on-line marketing,intelligence analysis,personalized recommendation,advertisement positioning,and communication prediction.Early influence prediction methods based on feature engineering established the relationship between different features and popularity by extracting and construct-ing key features.This paper focuses on the multi-class features related to social network influ-ence,and conducts research and review from the aspects of multi-class feature extraction,predic-tion model construction,and prediction evaluation methods,aiming to comprehensively analyze the existing re-search methods,and provide reference for improving the accuracy of social network influence prediction.[Meth-ods]Based on the current widely adopted deep learning methods,this paper summarizes and elaborates on the visu-al,textual,emotional,temporal,and user features of social networks by reviewing the literature,and analyzes the current research status and limitations of the influence prediction methods of social networks based on multi-class features.[Conclusions]With the development of deep learning theory,breakthrough progress has been made in deep feature extraction and prediction model construction,but at present,in terms of social network influence pre-diction,feature combination prediction methods based on multi-class features are still insufficient,and it is neces-sary to study more effective feature pre-extraction models to improve social network influence prediction accuracy.