@inproceedings{ff67d8c4d4894500ac7569f88bd38523,

title = "Same stats, different graphs: (Graph statistics and why we need graph drawings)",

abstract = "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{\textquoteright}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.",

keywords = "Graph generators, Graph mining, Graph properties",

author = "Hang Chen and Utkarsh Soni and Yafeng Lu and Ross Maciejewski and Stephen Kobourov",

year = "2018",

month = jan,

day = "1",

doi = "10.1007/978-3-030-04414-5_33",

language = "English (US)",

isbn = "9783030044138",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "463--477",

editor = "Therese Biedl and Andreas Kerren",

booktitle = "Graph Drawing and Network Visualization - 26th International Symposium, GD 2018, Proceedings",

note = "26th International Symposium on Graph Drawing and Network Visualization, GD 2018 ; Conference date: 26-09-2018 Through 28-09-2018",

}