Library and Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Institute of Journalism, Shanghai Academy of Social Science, Shanghai 200235, People's Republic of China.
Chaos. 2022 May;32(5):053127. doi: 10.1063/5.0086796.
User opinion affects the performance of network reconstruction greatly since it plays a crucial role in the network structure. In this paper, we present a novel model for reconstructing the social network with community structure by taking into account the Hegselmann-Krause bounded confidence model of opinion dynamic and compressive sensing method of network reconstruction. Three types of user opinion, including the random opinion, the polarity opinion, and the overlap opinion, are constructed. First, in Zachary's karate club network, the reconstruction accuracies are compared among three types of opinions. Second, the synthetic networks, generated by the Stochastic Block Model, are further examined. The experimental results show that the user opinions play a more important role than the community structure for the network reconstruction. Moreover, the polarity of opinions can increase the accuracy of inter-community and the overlap of opinions can improve the reconstruction accuracy of intra-community. This work helps reveal the mechanism between information propagation and social relation prediction.
用户意见对网络重构的性能影响很大，因为它在网络结构中起着至关重要的作用。在本文中，我们提出了一种新的模型，通过考虑 Hegselmann-Krause 有界置信模型的意见动态和网络重构的压缩感知方法，来重建具有社区结构的社交网络。构建了三种类型的用户意见，包括随机意见、极性意见和重叠意见。首先，在 Zachary 的空手道俱乐部网络中，比较了三种意见的重构精度。其次，进一步研究了由随机块模型生成的合成网络。实验结果表明，用户意见对于网络重构的作用比社区结构更为重要。此外，意见的极性可以提高社区间的准确性，而意见的重叠可以提高社区内的重构准确性。这项工作有助于揭示信息传播和社会关系预测之间的机制。
