基于邻接信息熵的网络节点重要性识别算法

通过研究节点与其直接相邻和间接相邻节点之间的关联关系,提出了基于邻接信息熵的网络节点重要性识别算法,算法只需获取节点与其直接邻居及间接邻居间的关联关系,通过计算网络各节点的邻接度,进而计算各节点的信息熵,利用节点信息熵的大小表征节点在网络中的重要性.通过对一个基础网络、无向无权ARPA网络和加权有向ARPA网络进行实验仿真,证明该算法对不同类型网络的通用性;利用该算法对网络按节点重要性进行节点删除实验,研究网络形成子网络的数量与规模,证明了算法的准确性.

Node importance recognition algorithm based on adjacency information entropy in networks

By studying the relationship between nodes and their direct and indirect adjacent nodes, an algorithm for identifying the importance of network nodes based on adjacent information entropy is explored. The algorithm only needs to extract the relationships between all nodes and their direct and indirect neighbors, and it can calculates the information-entropy of each node by the degree of adjacency of each node in the network, the numerical size of information-entropy shows the importance of nodes in the network. The applicability of the algorithm is expanded to simulate a basic network, an undirected weightless ARPA network and a weighted directed ARPA network. In addition, we prove the accuracy of the algorithm based on the number and scale of sub-networks formed, which can reflects the nature of the importance of deleted-node in the network.