Abstract

This work introduces an innovative methodology that amalgamates Graph Neural Networks (GNNs) with Reinforcement Learning (RL) to assess node significance in social networks. Conventional centrality metrics frequently neglect to reflect the dynamic characteristics of linkages in developing networks. This research advances the understanding of social dynamics by utilizing GNNs to produce intricate node embedding’s and applying RL to dynamically modify node importance based on interactions. The results illustrate the relevance of this hybrid paradigm in multiple fields, such as social media, business communities, public health, political mobilization, and innovation management, while tackling current issues in Social Network Analysis (SNA).

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