A Weighted Network Node Influence Analysis Method Based on K-shell
In response to the limitations in accuracy and resolution of existing key node identification methods,an improved K-shell ranking method is proposed. This method builds on traditional K-shell decomposition,integrating node degree,neighboring node influence,edge weights,and information entropy theory to refine the relative importance of nodes within the same K-shell level. Experimental results show that this method significantly improves the accuracy and monotonicity of rankings,effectively distinguishing the importance of nodes within the same K-shell level and accurately identifying key nodes with substantial network impact. This algorithm considers from multiple dimensions that affect the importance of key node identification,significantly improving accuracy and resolution. It holds great significance for the mining of key nodes in network anonymity and privacy protection.
万方数据
维普
