CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
4 Guilherme Avelino, Leonardo Passos, Andre Hora, and Marco Tulio Valente
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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citation-polarity summary
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2026 3verdicts
UNVERDICTED 3roles
dataset 1polarities
use dataset 1representative citing papers
A dependency-propagated impact model applied to 718k PyPI packages finds that 0.1% of them account for about 80% of total ecosystem impact from maintenance changes, differing from existing support lists.
Modeling projects as networks provides more consistent estimates of resilience to key personnel loss than existing methods.
citing papers explorer
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The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks
CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
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Modeling Dependency-Propagated Ecosystem Impact of Changes in Maintenance Activities: Evaluating Support Strategies in the PyPI Network
A dependency-propagated impact model applied to 718k PyPI packages finds that 0.1% of them account for about 80% of total ecosystem impact from maintenance changes, differing from existing support lists.
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Project resilience as network robustness
Modeling projects as networks provides more consistent estimates of resilience to key personnel loss than existing methods.