Extraction and Analysis of Sub-networks of Complex Networks-Based on Node Importance

Jia Sun, Jianbo Zhang
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Abstract

It is worthwhile to develop an effective method to identify the critical nodes in the complex network. Taking CBIN (Community-Based Important Node) as the parameter of “ node influence ”, and after identifying the influential influencers, a subnetwork of directional influencer network is set. In order to ensure the accuracy of the model, SI model is used to test the feasibility of CBIN. At last, four algorithms are compared. It is proved that CBIN is feasible and has advantages in measuring the influence of music.
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基于节点重要性的复杂网络子网络提取与分析
开发一种有效的方法来识别复杂网络中的关键节点是值得研究的。以CBIN (community - Important Node,基于社区的重要节点)作为“节点影响力”的参数,在识别有影响力的影响者后,设置指向性影响者网络的子网络。为了保证模型的准确性,采用SI模型对CBIN的可行性进行检验。最后对四种算法进行了比较。实验证明,CBIN方法是可行的,在测量音乐影响方面具有优势。
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