Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals' attributes (e.g. age, income), which are jointly informative of an individual's opinions and their social network position. Using a statistical mechanics based model of opinion formation, we show that it is possible to effectively influence dynamics on networks without full knowledge of network structure. In particular, influence strategies which use coarse grained network data, external covariates and point estimates of the state can all outperform a baseline model. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of running public information campaigns using insights from social network theory without costly or invasive levels of data collection.

中文翻译：

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在不了解网络微观结构的情况下影响社交网络的动态

基于社交网络的信息活动可用于促进有益的健康行为和缓解两极分化（例如关于气候变化或疫苗）。基于网络的干预策略通常依赖于对网络结构的充分了解。由于可用性和隐私问题，获得人口级别的社交网络数据在很大程度上是不可能或不可取的。更容易获得关于个人属性（例如年龄、收入）的信息，这些信息是个人观点及其社交网络位置的联合信息。使用基于统计力学的意见形成模型，我们表明可以在不完全了解网络结构的情况下有效地影响网络动态。特别是，使用粗粒度网络数据的影响策略，状态的外部协变量和点估计都可以胜过基线模型。我们的工作提供了一种可扩展的方法来影响大图上的 Ising 系统，并首次探索了存在环境（社会）场时的 Ising 影响问题。通过利用强环境场可以简化对网络动态的控制这一观察结果，我们的研究结果开启了使用社交网络理论的见解开展公共信息活动的可能性，而无需昂贵或侵入性的数据收集水平。