Super-resolution consists in reconstructing a high-resolution image from single or multiple low-resolution observations. Deep learning has been reported extremely successful for single-image super-resolution, but its applications to the multiple-image scenarios are limited due to the challenges that arise from feeding a network with a stack of images with sub-pixel translations. In this paper, we introduce Magnet—a new graph neural network that benefits from representing the input low-resolution images as a graph. This enables us to exploit the sub-pixel shifts among the input images while preserving the original low-resolution pixel values for feature extraction and information fusion. Despite a relatively simple architecture, Magnet outperforms the state-of-the-art methods for multiple-image super-resolution, and due to the flexible graph representation, it allows for using a variable number of low-resolution images for reconstruction.