Ante Pusic's blog post delves into the impact of disruptions within the global submarine internet cable network and demonstrates the application of dynamic betweenness centrality to analyze these effects using Memgraph. The blog highlights the significance of betweenness centrality as a metric for understanding traffic flow and node importance within a network, particularly under conditions of change, such as a cable cut. It introduces iCentral, an algorithm designed to efficiently update betweenness centrality scores in response to network changes without recalculating the entire graph, thus optimizing performance. The post illustrates how to set up Memgraph using Docker and GQLAlchemy, conduct analysis through a Jupyter notebook, and leverage the iCentral algorithm for real-time updates, drawing parallels with the Brandes algorithm. Practical examples showcase the effects of network changes on specific nodes, emphasizing the importance of efficient data processing in maintaining network stability and performance.