Same Stats, Different Graphs

(Graph Statistics and Why We Need Graph Drawings)
  • Hang Chen
  • Utkarsh Soni
  • Yafeng Lu
  • Ross Maciejewski
  • Stephen KobourovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11282)


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 (\(\le \)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.


Graph mining Graph properties Graph generators 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hang Chen
    • 1
  • Utkarsh Soni
    • 2
  • Yafeng Lu
    • 2
  • Ross Maciejewski
    • 2
  • Stephen Kobourov
    • 1
    Email author
  1. 1.University of ArizonaTucsonUSA
  2. 2.Arizona State UniversityTempeUSA

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