Maximal EF1 allocations exist for two agents under monotone valuations on any conflict graph and are computable in polynomial time for several cases, but fail to exist for three agents even with identical monotone valuations and are NP-hard to decide; EF[1,1] maximal allocations exist for identical非
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
citing papers explorer
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Fair Allocation under Conflict Constraints
Maximal EF1 allocations exist for two agents under monotone valuations on any conflict graph and are computable in polynomial time for several cases, but fail to exist for three agents even with identical monotone valuations and are NP-hard to decide; EF[1,1] maximal allocations exist for identical非
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Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.