“一站式”学生社区的社交网络分析方法

A Social Network Analysis Method for"One Stop"Student Community

为了对线上学生社区进行社交网络分析,文章提出了一种基于学生社交数据的孤立者检测方法,旨在识别社交网络中可能存在的孤立者。文章设计了一个包含连接特征、活跃度特征和中心性特征的特征提取层,通过综合分析构建了学生特征向量。随后,采用多层感知机作为神经网络层,对学生特征进行数据挖掘,最终输出学生为社交孤立者的概率。最后,以Friendster数据集为基础进行了实验,结果表明所提方法的准确度、精确度、召回率等较高。

In order to conduct social network analysis on online student communities,this paper proposes an outlier detection method based on student social data,aiming to identify potential outliers in social networks.This paper designs a feature extraction layer that includes connection features,activity features,and centrality features,and constructs student feature vectors through comprehensive analysis.Subsequently,a multi-layer perceptron is used as the neural network layer to mine student features and ultimately output the probability of students being social isolators.Finally,experiments are conducted on the basis of the Friendster dataset,and the results show that the proposed method has high accuracy,precision and recall rate.