Research map / Graph learning / Graph algorithms and clustering
Generalized Stochastic Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sampling with crucial- and non-crucial-edge procedures for unweighted matching | Generalized stochastic matching · Unweighted graphs | approximation ratio: .6568 - ε | Helped |
| Use the weighted crucial-edge procedure with sampling | Generalized stochastic matching · Weighted graphs | approximation ratio: .501- ε | Helped |
| Use an EDCS as the sparse subgraph | Generalized stochastic matching · Unweighted graphs | approximation ratio: 2/3 - O(ε) | Helped |
Closest papers by meaning
- Small Representations of Big Kidney Exchange Graphs (AAAI 2017 · similarity 0.89)
- Integrated Optimization of Bipartite Matching and Its Stochastic Behavior: New Formulation and Approximation Algorithm via Min-cost Flow Optimization (AAAI 2021 · similarity 0.87)
- Efficient Graph Matching for Correlated Stochastic Block Models (NeurIPS 2024 · similarity 0.84)
- Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS (ICML 2024 · similarity 0.84)
- Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract) (AAAI 2024 · similarity 0.83)
- MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation (ICML 2024 · similarity 0.82)
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