arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.04843physics.soc-phcs.SInlin.AO
收藏

复杂网络上的集群与观点动力学中的熵

Clusters and the entropy in opinion dynamics on complex networks

Wenchen Han, Yuee Feng, Xiaolan Qian, Qihui Yang, Changwei Huang

摘要:

在这项工作中,我们研究了复杂网络上的修改后的Hegselmann-Krause意见模型的异质人口。我们引入香农信息熵的所有相对意见集群来表征在最终配置的集群配置文件。独立于网络结构,存在一个子种群的最优聚集度,使最大的簇数和最高的熵。此外,对于最小的聚类数和最小的熵,存在一个子种群的最优有界置信度(或子种群比)。然而,网络结构确实影响集群配置文件。大的平均度有利于达成共识,使不同的网络与完全图更加相似。网络规模对无标度网络上异质种群的聚类分布影响有限,但对小世界网络上异质种群的聚类分布影响显著。

英文摘要:

In this work, we investigate a heterogeneous population in the modified Hegselmann-Krause opinion model on complex networks. We introduce the Shannon information entropy about all relative opinion clusters to characterize the cluster profile in the final configuration. Independent of network structures, there exists the optimal stubbornness of one subpopulation for the largest number of clusters and the highest entropy. Besides, there is the optimal bounded confidence (or subpopulation ratio) of one subpopulation for the smallest number of clusters and the lowest entropy. However, network structures affect cluster profiles indeed. A large average degree favors consensus for making different networks more similar with complete graphs. The network size has limited impact on cluster profiles of heterogeneous populations on scale-free networks but has significant effects upon those on small-world networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑