Exploring Influencer Dynamics and Network Resilience: A Deep Dive into Science-Related Subgraph of Twitter Ego Networks

Abstract

This paper presents an in-depth analysis of a Twitter ego network, focusing on the scientific community. Utilizing relative network analysis techniques, the study explores community structures, influencer dynamics, and network resilience. Key methodologies include community detection, centrality analysis, predictive modeling for link prediction and influence propagation, as well as resilience analysis. Results show distinct community formations, influential nodes, varying network resilience to disruptions. This comprehensive analysis provides valuable insights into the complex dynamics of scientific discourse on social media, emphasizing the importance of influential nodes and community structures in maintaining network integrity and facilitating information flow. This study will provide theoretical, methodological, and framework references for other social network analysis.