A Graph Neural Network Approach for Identification of Influencers and Micro-Influencers in a Social Network : *Classifying influencers from non-influencers using GNN and GCN

Targeting potential influencers for ad campaigns is one of the main challenges in social media marketing. This paper aims to incorporate Artificial Intelligence to segregate influencers from non-influencers. The target groups in focus are potential influencers. The authors build two different kinds of Graph Neural Networks, one is simple Graph Neural Network and the other is Graph Convolutional Network for node classification. A simple GNN model provides a convenient way of expressing node level, edge level and graph level prediction tasks. GCN classifies nodes based on their features. They compare the accuracy of these two models and propose the best model for social media targeting for ad campaigns, influencer detection. Additional research on Graphical models like Linear Threshold Model is being implemented to understand the importance of AI in detecting micro-influencers. Technology stack: Python and tensorflow.

I. Introduction

The ability to target individuals for ad campaigns on social media is referred to as social media targeting. Each group of people will have its own set of influencers. It will be considerably easier to market products once the influencers have been identified. Brands are increasingly turning to incorporate influencer marketing as a primary means of promoting and increasing brand awareness.