用合成控制法量化社交影响力，揭示朋友平均比你更有影响力
Measuring Social Influence with Networked Synthetic Control
- 结合机器学习与网络科学，用外部变量预测输出生成影响力合成度量
- 在各类网络中验证社会价值属性，发现朋友平均影响力高于个体
- 计算效率提升，适合研究政治行为等社交影响场景
由于缺乏反事实和对照，衡量社交影响力极具挑战。通过融合基于机器学习的建模与网络科学，我们提出了适用于政治行为的社会价值（Social Value）这一新型社交影响力度量方法，该方法基于合成控制。社会价值不同于中心性指标，其依赖外部回归变量预测目标输出，生成合成影响力度量，并根据社交网络分配个体贡献。通过理论推导，我们展示了在线性回归有无交互项的情况下，社会价值在格状网络、幂律网络和随机图中的性质。任何集成模型均可实现计算效率提升。模拟结果表明，广义友谊悖论成立——在某些情境下，你的朋友平均比你拥有更高的影响力。
原文摘要 · Abstract (English)
Measuring social influence is difficult due to the lack of counter-factuals and comparisons. By combining machine learning-based modeling and network science, we present general properties of social value, a recent measure for social influence using synthetic control applicable to political behavior. Social value diverges from centrality measures on in that it relies on an external regressor to predict an output variable of interest, generates a synthetic measure of influence, then distributes individual contribution based on a social network. Through theoretical derivations, we show the properties of SV under linear regression with and without interaction, across lattice networks, power-law networks, and random graphs. A reduction in computation can be achieved for any ensemble model. Through simulation, we find that the generalized friendship paradox holds -- that in certain situations, your friends have on average more influence than you do.
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