论文标题
分类性对异质剂共识形成的影响
Effects of Assortativity on Consensus Formation with Heterogeneous Agents
论文作者
论文摘要
尽管Barabasi的无标度网络和ERDOS-RENYI网络广泛使用了该程度相关性（分类性）是中性的，但许多研究表明，在线社交网络倾向于显示分类混合（正性程度相关性），而非社会网络则显示出分层的混合（负相关）。首先，我们通过使用REDDIT中的三个不同的子列表分析了同一平台不同组不同组的分类系数的可变性。我们的数据分析结果表明，REDDIT是分解的，上述子列站的分类系数分别为-0.0384，-0.0588和-0.1107。由于结果的变异性，即使在同一平台中，我们也决定研究共识形成动力学对网络分类性的敏感性。我们得出的结论是，当网络拆分混合或中立时，系统更有可能达成共识。但是，当网络分类时，共识的可能性大大降低。令人惊讶的是，与各种或分类网络相比，当网络处于中立时，所有节点都过去的时间略低。当药剂的阈值分布得更加异质时，这些结果会更明显。
Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi networks of which degree correlation (assortativity) is neutral, numerous studies demonstrated that online social networks tend to show assortative mixing (positive degree correlation), while non-social networks show a disassortative mixing (negative degree correlation). First, we analyzed the variability in the assortativity coefficients of different groups of the same platform by using three different subreddits in Reddit. Our data analysis results showed that Reddit is disassortative, and assortativity coefficients of the aforementioned subreddits are computed as -0.0384, -0.0588 and -0.1107, respectively. Motivated by the variability in the results even in the same platform, we decided to investigate the sensitivity of dynamics of consensus formation to the assortativity of the network. We concluded that the system is more likely to reach a consensus when the network is disassortatively mixed or neutral; however, the likelihood of the consensus significantly decreases when the network is assortatively mixed. Surprisingly, the time elapsed until all nodes fix their opinions is slightly lower when the network is neutral compared to either assortative or disassortative networks. These results are more pronounced when the thresholds of agents are more heterogeneously distributed.
