Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method

In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which based on the network structure features. First, obtained multi-dimensional data students' identities, seating relationships, social and so to build database. Then, similarity among students was used construct in-class networks analyze structural characteristics them. Finally, CRITIC entropy weight introduced for obtaining values multi-decision fusion mine student nodes that have negative impact. The experiments showed paper can evaluate objectively their classroom networks, providing technical support process-oriented comprehensive quality education evaluation.