Top Persuader Prediction for Social Networks1
作者研究了社交网络中的“顶级说服者”预测问题。顶级说服者是指那些一旦采纳某产品或服务，就能带动最多其他用户也采纳的节点。准确预测这类节点对病毒营销、客户留存等应用至关重要。现有方法主要关注社会影响力，而作者基于社会网络理论，整合了社会影响、用户相似性和结构等价性三种说服力，提出了“说服概率”概念，并据此开发了预测算法。通过真实社交网络数据评估，作者的方法显著优于主流方法和行业实践。
Top persuaders in a social network are social entities whose adoption of a product or service will result in the largest numbers of other entities in the network adopting the same product or service. Predicting top persuaders is critical to an expanding array of important social network-centric applications, such as viral marketing, customer retention, and political message promotion. This study formulates the top persuader prediction problem and develops a novel method to predict top persuaders. Our method development is rooted in eminent social network theories that reveal several forces central to social persuasion, including social influence, entity similarity, and structural equivalence. Our method innovatively integrates these forces to predict top persuaders in a social network, in contrast to existing methods that primarily focus on social influence. Specifically, we introduce persuasion probability that denotes the likelihood of persuasion conditioned on these forces. We then propose how to estimate persuasion probabilities, develop an algorithm to predict top persuaders using the estimated persuasion probabilities, and analyze the theoretical property of the algorithm. We conduct an extensive evaluation with real-world social network data and show that our method substantially outperforms prevalent methods from representative previous research and salient industry practices.