5 Scopus citations


Data analysts commonly utilize statistics to summarize large datasets. While it is often sufficient to explore only the summary statistics of a dataset (e.g., min/mean/max), Anscombe’s Quartet demonstrates how such statistics can be misleading. We consider a similar problem in the context of graph mining. To study the relationships between different graph properties and statistics, we examine all low-order (≤10) non-isomorphic graphs and provide a simple visual analytics system to explore correlations across multiple graph properties. However, for graphs with more than ten nodes, generating the entire space of graphs becomes quickly intractable. We use different random graph generation methods to further look into the distribution of graph statistics for higher order graphs and investigate the impact of various sampling methodologies. We also describe a method for generating many graphs that are identical over a number of graph properties and statistics yet are clearly different and identifiably distinct.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 26th International Symposium, GD 2018, Proceedings
EditorsTherese Biedl, Andreas Kerren
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030044138
StatePublished - 2018
Event26th International Symposium on Graph Drawing and Network Visualization, GD 2018 - Barcelona, Spain
Duration: Sep 26 2018Sep 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11282 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other26th International Symposium on Graph Drawing and Network Visualization, GD 2018


  • Graph generators
  • Graph mining
  • Graph properties

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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