Computing Betweenness Centrality in Link Streams
This work addresses a computational bottleneck for analyzing dynamic networks in fields like social network analysis or communication systems, though it is incremental as it extends an existing centrality concept to a more complex setting.
The authors tackled the problem of computing betweenness centrality in link streams, a time-dependent graph generalization, by developing the first polynomial-time algorithms for this task and providing an implementation with illustrative examples.
Betweeness centrality is one of the most important concepts in graph analysis. It was recently extended to link streams, a graph generalization where links arrive over time. However, its computation raises non-trivial issues, due in particular to the fact that time is considered as continuous. We provide here the first algorithms to compute this generalized betweenness centrality, as well as several companion algorithms that have their own interest. They work in polynomial time and space, we illustrate them on typical examples, and we provide an implementation.