# Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations Source: [Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:59:56.170Z Authors: Naman Goyal, David Steiner Published: 2022-03-07T15:16:31.000Z ## Abstract In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a graph-level prediction graph, specifically image classification. For the reinforcement learning section, we seek to learn if explicitly modeling solving a Rubik's cube as a graph problem can improve performance over a standard model-free technique with no inductive bias. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()