Symmetry Detection and Classification in Drawings of Graphs

Felice De Luca, Md Iqbal Hossain, Stephen Kobourov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 27th International Symposium, GD 2019, Proceedings
EditorsDaniel Archambault, Csaba D. Tóth
Number of pages15
ISBN (Print)9783030358013
StatePublished - 2019
Externally publishedYes
Event27th International Symposium on Graph Drawing and Network Visualization, GD 2019 - Prague, Czech Republic
Duration: Sep 17 2019Sep 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11904 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Symposium on Graph Drawing and Network Visualization, GD 2019
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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