  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.
