Summary of Graph Neural Networks As Ordering Heuristics For Parallel Graph Coloring, by Kenneth Langedal and Fredrik Manne
Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring
by Kenneth Langedal, Fredrik Manne
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed graph neural network (GNN) based ordering heuristic outperforms existing greedy heuristics for solving the NP-hard graph coloring problem. This task involves assigning the minimum number of distinct colors to vertices in an undirected graph, with no adjacent vertices sharing the same color. The GNN model is trained using both supervised and unsupervised techniques, achieving execution times between those of the largest degree first (LF) and smallest degree last (SL) ordering heuristics while producing higher-quality colorings. Increasing the number of layers improves coloring quality further, with a 2-layer model achieving superior performance compared to SL and LF. The GNN-based heuristic also demonstrates better parallel scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to solve the graph coloring problem using artificial intelligence. This is an important task that has many real-world applications. The authors train a special kind of neural network called a graph neural network (GNN) to help with this problem. They show that their GNN can produce better results than other methods, while also being faster and more efficient. This is especially useful because the GNN model can be used in parallel settings, making it even more powerful. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Neural network » Supervised » Unsupervised
