Keywords: influencer detection, dynamic graph learning, graph convolutional networks, graph attention networks
TL;DR: This paper investigates different dynamic GNNs configurations for influencer detection using a unique corporate data set.
Abstract: 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.
Paper Format: short paper (4 pages)