一种基于K-shell和半局部信息的节点重要性排序方法

Method of Node Importance Ranking Based on K-shell and Semi-local Information

对复杂网络中的节点按照其重要性进行排序不仅具有重要的理论研究意义而且具有广泛的实际应用价值。传统的K-shell分解方法虽然具有较好的排序结果,但仍然具有排序结果分辨率不高的缺陷,针对这一问题,对传统的K-shell分解方法进行改进,并综合节点的半局部信息,进一步区分节点的重要性。在三个不同的显示网络中的实验表明,该方法能够有效解决传统方法的缺陷,提高排序结果分辨率。

Ranking the importance of nodes in the complex networks has both great theoretical significance and the wide range of application. The traditional K-shell decomposition method has the defect of the sorting result with low resolution. In order to solve this problem, this paper proposed an improved K-shell method integrated with semi-local information to further distinguish the importance of different nodes. Experiments on three real networks show that the proposed method can effectively solve the defects of traditional methods.