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Approximating Betweenness-Centrality ranking
3260 papers • 126 benchmarks • 313 datasets
Betweenness-centrality is a popular measure in network analysis that aims to describe the importance of nodes in a graph. It accounts for the fraction of shortest paths passing through that node and is a key measure in many applications including community detection and network dismantling.
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ABCDE: Approximating Betweenness-Centrality ranking with progressive-DropEdge
Martin Mirakyan•Sun Sep 05 2021
This work presents a deep graph convolutional neural network that outputs a rank score for each node in a given graph that is an order of magnitude faster in inference and requires several times fewer resources and time to train.
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