Скачать или смотреть GRAPH NEURAL NETWORKS FOR SOCIAL NETWORK ANALYSIS IN INDIA DETECTING FAKE PROFILES AND BOTNETS

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Abstract:
The rapid growth of social networks in India has amplified the challenge of detecting fake profiles and coordinated botnets that manipulate public opinion, spread misinformation, and compromise online security. Traditional detection methods often fail to capture complex relational patterns inherent in social graphs. This study proposes a novel approach leveraging Graph Neural Networks (GNNs) to analyze social network structures and user behavior for effective identification of fake accounts and botnets. By modeling users as nodes and interactions as edges, the GNN framework learns rich representations that incorporate both network topology and node features. Using real-world Indian social media datasets, the proposed model demonstrates superior accuracy and robustness compared to conventional machine learning techniques. The findings highlight the potential of GNN-based solutions for enhancing the integrity of social platforms in India, while addressing challenges related to data privacy, scalability, and evolving adversarial tactics