Influencer Detection with Dynamic Graph Neural Networks
This work addresses influencer detection for targeted marketing, presenting an incremental improvement using dynamic GNNs.
The paper tackled influencer detection by investigating dynamic Graph Neural Networks (GNNs) on a corporate dataset, showing that deep multi-head attention and temporal encoding improve performance, with neighborhood representation outperforming network centrality measures.
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.