Copyright Notice:

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Publications of SPCL

L. Gianinazzi, M. Fries, N. Dryden, T. Ben-Nun, M. Besta, T. Hoefler:

 Learning Combinatorial Node Labeling Algorithms

(arXiv:2106.03594. Jun. 2021)


We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph coloring. We train our model using reinforcement learning, specifically policy gradients, which gives us both a greedy and a probabilistic policy. Our architecture builds on a graph attention network and uses several inductive biases to improve solution quality. Our learned deterministic heuristics for graph coloring give better solutions than classical degree-based greedy heuristics and only take seconds to apply to graphs with tens of thousands of vertices. Moreover, our probabilistic policies outperform all greedy state-of-the-art coloring baselines and a machine learning baseline. Finally, we show that our approach also generalizes to other problems by evaluating it on minimum vertex cover and outperforming two greedy heuristics.


download article:
access preprint on arxiv:


  author={Lukas Gianinazzi and Maximilian Fries and Nikoli Dryden and Tal Ben-Nun and Maciej Besta and Torsten Hoefler},
  title={{Learning Combinatorial Node Labeling Algorithms}},