  Symmetry is a key feature observed in nature (from flowers and leaves, to
butterflies and birds) and in human-made objects (from paintings and
sculptures, to manufactured objects and architectural design). Rotational,
translational, and especially reflectional symmetries, are also important in
drawings of graphs. Detecting and classifying symmetries can be very useful in
algorithms that aim to create symmetric graph drawings and in this paper we
present a machine learning approach for these tasks. Specifically, we show that
deep neural networks can be used to detect reflectional symmetries with 92%
accuracy. We also build a multi-class classifier to distinguish between
reflectional horizontal, reflectional vertical, rotational, and translational
symmetries. Finally, we make available a collection of images of graph drawings
with specific symmetric features that can be used in machine learning systems
for training, testing and validation purposes. Our datasets, best trained ML
models, source code are available online.
