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建模已在众多领域广泛应用。在推荐营销和定向营销领域，由于客户-品牌关系的持续演变，动态网络表示的引入能够极大助力影响者识别这一重要研究方向。为阐述这一理念，我们提出INFLECT-DGNN——一种基于动态图神经网络的影响者预测新框架，该框架融合图神经网络与循环神经网络，采用加权损失函数、面向图数据适配的合成少数类过采样技术，以及精心设计的滚动窗口策略。为评估预测性能，我们利用包含三座城市网络的独特企业数据集，并推导出面向利润的影响者预测评估方法。实验结果表明，通过RNN编码时序特征结合GNN可显著提升预测性能。通过比较多种模型的结果，我们论证了捕捉图结构表示、时序依赖关系以及采用利润导向评估方法的重要性。