pith:63Q6LYCD
Graph Neural Networks with Triangle-Based Messages for the Multicut Problem
A graph neural network that passes messages only along triangles in edge-featured graphs finds higher-quality solutions to the multicut problem than existing heuristic solvers.
arxiv:2605.13673 v1 · 2026-05-13 · cs.LG
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Claims
Experiments with synthetic and real-world instances with up to 200 nodes show that our method outperforms state-of-the-art heuristic solvers in terms of solution quality while maintaining feasible runtimes. For some instances, our method finds optimal solutions in seconds whereas exact solvers need hours to find and certify optimal solutions.
The assumption that training the triangle-message GNN on the provided synthetic and real-world instances produces a model that generalizes reliably to new instances of the multicut problem without overfitting to the training distribution or requiring extensive hyperparameter tuning.
A triangle-message GNN for multicut outperforms heuristics in solution quality on graphs up to 200 nodes and finds optimal solutions faster than exact solvers for some cases.
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| First computed | 2026-05-18T02:44:17.142046Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f6e1e5e043aa4f3ab6cf1572146e15e2f21d96ce9d94b21fdb3d03918bcc406d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/63Q6LYCDVJHTVNWPCVZBI3QV4L \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f6e1e5e043aa4f3ab6cf1572146e15e2f21d96ce9d94b21fdb3d03918bcc406d
Canonical record JSON
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