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graph coarsening

topic2 events
papersTODAY 04:00 UTC

Geometric Flow Method Improves Graph Coarsening for GNN Pooling

A new arXiv paper proposes using geometric flow techniques to enhance graph coarsening, a pooling step used in graph convolutional networks to cut computational cost. The approach aims to reduce the expense of graph pooling operations that mirror pooling in standard convolutional networks. The work is a preprint and reports on method design rather than deployed results.

graph-neural-networks/arxiv/efficiency/research
papersSEP 10 04:00 UTC

GNN-guided graph coarsening cuts QUBO size for quantum-annealed vehicle routing

An arXiv paper pairs graph neural network-guided coarsening with adaptive penalty tuning to shrink the QUBO formulations that arise when capacitated vehicle routing problems with time windows are solved on a quantum annealer. Customers that are geographically close and have compatible delivery windows are merged into super-nodes, reducing the number of binary variables before the problem reaches the annealer. The goal is to make quantum annealing tractable for larger, more realistic logistics instances.

quantum/gnn/optimization/logistics