INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks. (arXiv:2307.08131v2 [cs.SI] UPDATED)

Leveraging network information for predictive modeling has become widespread
in many domains. Within the realm of referral and targeted marketing,
influencer detection stands out as an area that could greatly benefit from the
incorporation of dynamic network representation due to the ongoing development
of customer-brand relationships. To elaborate this idea, we introduce
INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph
Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural
Networks (RNN) with weighted loss functions, the …

arxiv become benefit brand customer detection development domains dynamic graph graph neural networks influencer information marketing modeling network networks neural networks prediction predictive predictive modeling relationships representation