The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging, due to confounds like homophily and practical issues like missing data. In this work, we propose a novel discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. Using synthetically generated data, we provide empirical evidence that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification --- unlike popular alternatives. We also apply the method to two real-world datasets: (1) cascades of co-sponsorship of legislation in the U.S. House of Representatives, on a social network of shared campaign donors; (2) rumors about the Higgs boson discovery, on a follower network of $10^5$ Twitter accounts. Our model identifies the role of peer influence in these scenarios, and uses it to make more accurate predictions about the future trajectory of cascades.

提出一种基于排名算法的简单判别方法，以检测观测数据中的社交影响，并将其应用于两个真实数据集，包括美国众议院议员在共享竞选捐助者的社交网络上的联合赞助和 $10^5$ 个 Twitter 帐户的追随者网络的关于 Higgs 玻色子发现的谣言。该方法能够准确地识别社交影响的角色并使用其进行更准确的级联未来轨迹预测，且在存在混杂因素、缺失数据和误差模型等情况下具有较强的鲁棒性。

通过比较区分排名器检测事件级联中的社交影响