TL;DR
This paper introduces ONBRA, a sampling-based algorithm that efficiently estimates temporal betweenness centrality in networks with temporal data, providing rigorous probabilistic guarantees and outperforming exact methods in computational efficiency.
Contribution
ONBRA is the first approximation algorithm with theoretical guarantees for temporal betweenness centrality, addressing computational challenges in analyzing large temporal networks.
Findings
ONBRA significantly reduces computation time compared to exact methods.
It provides high-quality estimates with rigorous probabilistic guarantees.
Experimental results on real networks validate its efficiency and accuracy.
Abstract
In network analysis, the betweenness centrality of a node informally captures the fraction of shortest paths visiting that node. The computation of the betweenness centrality measure is a fundamental task in the analysis of modern networks, enabling the identification of the most central nodes in such networks. Additionally to being massive, modern networks also contain information about the time at which their events occur. Such networks are often called temporal networks. The temporal information makes the study of the betweenness centrality in temporal networks (i.e., temporal betweenness centrality) much more challenging than in static networks (i.e., networks without temporal information). Moreover, the exact computation of the temporal betweenness centrality is often impractical on even moderately-sized networks, given its extremely high computational cost. A natural approach to…
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