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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Elevating Social Network Analysis with a Graph Network and Reinforcement Learning Integration for Node Importance

by Yusra Khan, Ubaida Fatima
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 55
Year of Publication: 2024
Authors: Yusra Khan, Ubaida Fatima
10.5120/ijca2024924279

Yusra Khan, Ubaida Fatima . Elevating Social Network Analysis with a Graph Network and Reinforcement Learning Integration for Node Importance. International Journal of Computer Applications. 186, 55 ( Dec 2024), 61-70. DOI=10.5120/ijca2024924279

@article{ 10.5120/ijca2024924279,
author = { Yusra Khan, Ubaida Fatima },
title = { Elevating Social Network Analysis with a Graph Network and Reinforcement Learning Integration for Node Importance },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 55 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 61-70 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number55/elevating-social-network-analysis-with-a-graph-network-and-reinforcement-learning-integration-for-node-importance/ },
doi = { 10.5120/ijca2024924279 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-27T02:45:45.186568+05:30
%A Yusra Khan
%A Ubaida Fatima
%T Elevating Social Network Analysis with a Graph Network and Reinforcement Learning Integration for Node Importance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 55
%P 61-70
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Graph Neural Networks Reinforcement Learning Node Importance Social Network Analysis Dynamic Networks

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