基于排序学习的复杂网络节点接近中心性近似排序

随着网络规模的增大,节点接近中心性的精确算法效率越来越低.本文提出一种基于RankNet排序学习算法的模型以快速逼近复杂网络节点接近中心性排序.首先通过相关性分析得到与接近中心性呈正相关的节点重要度指标作为模型的输入特征,然后在给定网络中随机选取节点子集用于模型的训练样本数据.在一个真实航空网络数据集和典型的复杂网络模型上对提出的模型进行了验证,实验结果表明基于RankNet排序学习算法的模型能够在一定程度上降低计算时间复杂度,而且保持了较高的近似准确性,所提出的模型排序效果明显优于采用回归学习的基准模型.

Approximate Rank of Closeness Centrality of Complex Network Nodes Based on Learning to Rank

As the network expands, the exact algorithm of closeness centrality has low efficiency. In this study, a model based on the learning to rank algorithm (RankNet) is proposed to quickly approximate the closeness centrality rank of complex network nodes. Firstly, the study carries out a correlation analysis to obtain important node indicators positively correlated with the closeness centrality and put them as input features of the model. Subsequently, a subset of nodes in a given network is randomly selected and used for the training sample data of the model. The proposed model is verified by a real aviation network dataset and typical complex network models. The experimental results show that the RankNet-based model not only reduces the computational complexity but also keeps a high accuracy of the approximation. In addition, the ranking performance of the proposed model is significantly superior to that of the benchmark model based on regression learning.