Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out as an area that could greatly benefit from the incorporation of dynamic network representation due to the ongoing development of customer-brand relationships. To elaborate this idea, we introduce INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) with weighted loss functions, the Synthetic Minority Oversampling TEchnique (SMOTE) adapted for graph data, and a carefully crafted rolling-window strategy. To evaluate predictive performance, we utilize a unique corporate data set with networks of three cities and derive a profit-driven evaluation methodology for influencer prediction. Our results show how using RNN to encode temporal attributes alongside GNNs significantly improves predictive performance. We compare the results of various models to demonstrate the importance of capturing graph representation, temporal dependencies, and using a profit-driven methodology for evaluation.

翻译：利用网络信息进行预测性建模已在众多领域得到广泛应用。在推荐营销和目标营销领域，由于客户-品牌关系的持续演变，影响者检测成为能够从动态网络表示中显著受益的研究方向。为深入阐述这一思想，我们提出INFLECT-DGNN（基于动态图神经网络的影响者预测新框架），该框架融合了图神经网络（GNN）与循环神经网络（RNN），并引入加权损失函数、面向图数据改进的合成少数类过采样技术（SMOTE），以及精心设计的滚动窗口策略。为评估预测性能，我们采用包含三个城市网络的独特企业数据集，并推导出基于利润驱动的影响者预测评估方法。实验结果表明，利用RNN编码时序特征与GNN相结合可显著提升预测性能。通过对比多种模型的结果，我们验证了捕捉图结构表示、时序依赖性以及采用利润驱动评估方法的重要性。