基于有限信息的网络邻接关系识别研究

如何根据有限的网络交互信息确定网络结构是当前网络科学研究的重要问题之一。该文提出了基于网络单一结构属性的网络结构重构分析方法。首先，该方法通过Holme-Kim模型生成一系列集聚系数可调的人工网络。然后，该方法通过压缩感知识别模型对有限信息下的网络结构进行重构。实验结果表明，当仅能掌握20%的网络节点间交互的时间序列信息时，网络邻接关系的平均识别准确率和平均真实关系召回率均随网络平均集聚系数的增大而提高。当网络的平均集聚系数在0.1~0.6变化时，平均集聚系数为0.6的网络将获得最高的平均识别准确率和平均真实关系召回率。进一步的实验分析说明，网络中度小于8的节点其平均识别准确率是决定网络平均识别准确率的关键。

The Identification of Networks' Adjacent Connective Relationships Based on Limited Information

To uncover the networks' structure according to limited interactional information is one of the significant problems in the field of network science. We develop a method for reconstructing and analyzing the networks' structure based on single structure attribute of network. First, a series of synthetic networks with tunable clustering coefficient are generated under Holme-Kim model. Then, the networks' structure is reconstructed by virtue of a compressive-sensing identification model with limited information. Experimental results demonstrate that when we have 20% time-series information of interactions between nodes in the networks, both the average identification accuracy and the average recall of existent links of the whole networks would be increased with the increment of the average clustering coefficient. In this paper, the average clustering coefficient of the target networks is varying from 0.1 to 0.6. The average identification accuracy and the average recall of existent links would reach the optimum value when the network's average clustering coefficient is 0.6. Further, we make a deeper investigation into the experimental data. We find that the average identification accuracy of the networks largely depends on that of the corresponding nodes, whose degree is less than 8 in networks.