Influencer Detection with Dynamic Graph Neural Networks

2022/11/15 by Elena Tiukhova, Emiliano Penaloza, Tiukhova, Elena +13
Computer Science · Physics and Astronomy · Social Sciences · #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Digital Marketing and Social Media #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2211.09664

openalex publication_date 2022/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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