Influencer Detection with Dynamic Graph Neural Networks
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.
Cite
Text
Tiukhova et al. "Influencer Detection with Dynamic Graph Neural Networks." NeurIPS 2022 Workshops: TGL, 2022.Markdown
[Tiukhova et al. "Influencer Detection with Dynamic Graph Neural Networks." NeurIPS 2022 Workshops: TGL, 2022.](https://mlanthology.org/neuripsw/2022/tiukhova2022neuripsw-influencer/)BibTeX
@inproceedings{tiukhova2022neuripsw-influencer,
title = {{Influencer Detection with Dynamic Graph Neural Networks}},
author = {Tiukhova, Elena and Penaloza, Emiliano and Óskarsdóttir, María and Garcia, Hernan and Bahnsen, Alejandro Correa and Baesens, Bart and Snoeck, Monique and Bravo, Cristián},
booktitle = {NeurIPS 2022 Workshops: TGL},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/tiukhova2022neuripsw-influencer/}
}