Influence detection in dynamic networks: a novel overlapping community detection approach applied to COVID-19 spread analysis in India

Influential node detection is crucial for understanding and managing real-world networks, such as social, biological, and information networks, as it helps identify key participants and network dynamics. This paper introduces a novel approach, the Internal-External Overlapping Community Detection method, which aims to uncover overlapping communities within networks to identify influential nodes. We propose a new metric, the Influence Detection Factor, designed to pinpoint nodes that significantly impact network behavior and evolution. By examining state transitions in time-varying networks, our approach provides valuable insights into how these influential nodes drive changes over time, contributing to a deeper understanding of network resilience and adaptation. As a case study, we analyze the spread of COVID-19 in India, where provinces are represented as nodes and number of cases as edge weights. We compare our method against traditional centrality measures, such as degree, closeness, and betweenness centrality, demonstrating that our approach aligns more closely with real-world epidemiological data and offers superior modularity, and higher F1 scores. Our experimental results underscore the efficacy of the proposed method in capturing and forecasting network behavior, making it a powerful tool for dynamic social network analysis and providing actionable insights for public health interventions and epidemic management strategies.