Graph Drawing

Volume 7704 of the series Lecture Notes in Computer Science pp 2-18

Interactive Network Exploration to Derive Insights: Filtering, Clustering, Grouping, and Simplification

  • Ben ShneidermanAffiliated withUniversity of Maryland
  • , Cody DunneAffiliated withUniversity of Maryland

* Final gross prices may vary according to local VAT.

Get Access


The growing importance of network analysis has increased attention on interactive exploration to derive insights and support personal, business, legal, scientific, or national security decisions. Since networks are often complex and cluttered, strategies for effective filtering, clustering, grouping, and simplification are helpful in finding key nodes and links, surprising clusters, important groups, or meaningful patterns. We describe readability metrics and strategies that have been implemented in NodeXL, our free and open source network analysis tool, and show examples from our research. While filtering, clustering, and grouping have been used in many tools, we present several advances on these techniques. We also discuss our recent work on motif simplification, in which common patterns are replaced with compact and meaningful glyphs, thereby improving readability.


Network visualization visual analytics readability metrics dynamic filters link clustering attribute grouping motif simplification