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Abstract
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Given the increasing effect of social networks on public opinion and behavior, it is imperative to accurately identify significant influencers. Finding influencers is a vital area of study since they are essential to marketing, legislative lobbying, and social movements. Traditional approaches are effective at mapping network topologies, which is a growing concern as data breaches and misuse occur more frequently. This paper proposes a novel mix of advanced deep learning techniques and secure compute methodologies to bridge this gap and accomplish high-accuracy influencer detection
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