论文标题
使用子图TR-ENCERATIO中的复杂网络中节点评估的新观点
A New Perspective to Node Influence Evaluation in Complex Network Using Subgraph Tr-Centrality
论文作者
论文摘要
评估节点在复杂网络中的影响排名具有重要意义。多年来，许多研究人员提出了不同的措施来量化网络中的节点互连性。因此，本文引入了一种称为TR-伦敦的中心度度量，该措施的重点是使用节点三角形结构和节点邻域信息来定义节点的强度，该节点的强度定义为gruebler对节点的单跳三角邻域的求和，以将其单跳三角邻域邻域的数量定义为图中所有边缘的数量。此外，我们在社会上将其视为节点的本地信任。为了验证TR中心性的有效性[1]，我们将其应用于具有不同密度和形状的四个现实世界网络，而TR-Centrality已证明可以产生更好的结果。
There is great significance in evaluating a node's Influence ranking in complex networks. Over the years, many researchers have presented different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighborhood information to define the strength of a node, which is defined as the summation of Gruebler's Equation of the node's one-hop triangle neighborhood to the number of all the edges in the subgraph. Furthermore, we socially consider it as the local trust of a node. To verify the validity of Tr-centrality [1], we apply it to four real-world networks with different densities and shapes, and Tr-centrality has proven to yield better results.
