  Leveraging network information for prediction tasks has become a common
practice in many domains. Being an important part of targeted marketing,
influencer detection can potentially benefit from incorporating dynamic network
representation. In this work, we investigate different dynamic Graph Neural
Networks (GNNs) configurations for influencer detection and evaluate their
prediction performance using a unique corporate data set. We show that using
deep multi-head attention in GNN and encoding temporal attributes significantly
improves performance. Furthermore, our empirical evaluation illustrates that
capturing neighborhood representation is more beneficial that using network
centrality measures.
