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Accelerating Spanning-Centrality Approximation with Sampling-Aware Vectorized Random Walks
Spanning centrality quantifies an edge's importance through its share of a graph's spanning trees, and approximating it for all edges (AESC) requires processing large volumes of irregular random walks under a prescribed error bound. Its applications include phylogenetic analysis and network robustne
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