论文标题
社交网络上意见动态的游戏理论方法
Game-theoretical approach for opinion dynamics on social networks
论文作者
论文摘要
近年来，人们对社交网络的意见动态已经受到了很多关注。然而，理论上只有一些作品分析了一定意见可以在整个结构化人群中传播的状况。在本文中，我们为二进制意见模型提出了一种进化游戏方法，以探索意见传播的条件。受到现实观察的启发，我们假设代理商选择意见的选择不是随机的，而是基于源自公共知识和与邻居的互动的分数。通过融合随机步行，我们获得了一个条件，即$ a $可以在薄弱的选择限制中传播在社交网络上。我们发现，意见的成功传播条件$ a $与二进制意见的基本分数，意见相互作用的反馈分数以及包括边缘权重，顶点的加权程度以及网络的平均程度在内的结构参数相关。特别是，当个人仅根据公共信息调整意见时，意见的活力$ a $仅取决于$ a $ a $ and $ b $的基本分数差异。当连接个人之间没有负面的反馈相互作用时，我们发现意见$ a $的成功取决于获得的竞争意见的正面（负）反馈分数的比率。为了完成我们的研究，我们分别对完全连接，小世界和无规模网络进行计算机模拟，以支持和确认我们的理论发现。
Opinion dynamics on social networks have been received considerable attentions in recent years. Nevertheless, just a few works have theoretically analyzed the condition in which a certain opinion can spread in the whole structured population. In this paper, we propose an evolutionary game approach for a binary opinion model to explore the conditions for an opinion's spreading. Inspired by real-life observations, we assume that an agent's choice to select an opinion is not random, but is based on a score rooted both from public knowledge and the interactions with neighbors. By means of coalescing random walks, we obtain a condition in which opinion $A$ can be favored to spread on social networks in the weak selection limit. We find that the successfully spreading condition of opinion $A$ is closely related to the basic scores of binary opinions, the feedback scores on opinion interactions, and the structural parameters including the edge weights, the weighted degrees of vertices, and the average degree of the network. In particular, when individuals adjust their opinions based solely on the public information, the vitality of opinion $A$ depends exclusively on the difference of basic scores of $A$ and $B$. When there are no negative (positive) feedback interactions between connected individuals, we find that the success of opinion $A$ depends on the ratio of the obtained positive (negative) feedback scores of competing opinions. To complete our study, we perform computer simulations on fully-connected, small-world, and scale-free networks, respectively, which support and confirm our theoretical findings.
