Influencer Detection with Dynamic Graph Neural Networks

Autor:	Tiukhova, Elena, Penaloza, Emiliano, Óskarsdóttir, María, Garcia, Hernan, Bahnsen, Alejandro Correa, Baesens, Bart, Snoeck, Monique, Bravo, Cristián
Rok vydání:	2022
Předmět:	Computer Science - Social and Information Networks Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Conference workshop camera-ready paper - accepted at NeurIPS TGL 2022. 8 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.09664 Zobrazit plný text záznamu View this record from Arxiv