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.

这篇论文提出了一种基于动态图神经网络的影响力预测框架，并结合了权重损失函数、SMOTE 和滚动窗口策略等方法来改善预测性能；作者使用了独有的企业数据集，其中包含三个城市的网络，并提出了一种基于利润的评估方法，结果表明采用 RNN 编码时序属性可以显著提高预测性能，捕捉图表示形式、时序依赖关系以及使用基于利润的方法进行评估的重要性。