Elevating Social Network Analysis with a Graph Network and Reinforcement Learning Integration for Node Importance
Year: 2024
Type: article
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).
Authors: Yusra Khan, Ubaida Fatima
Institution: NED University of Engineering and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405846975