Interactive network exploration to derive insights: filtering, clustering, grouping, and simplification 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.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Kaplan, Andee; Hofmann, Heike; Nordman, Daniel: An interactive graphical method for community detection in network data (2017)
- Safaei, Farshad; Sotoodeh, Hamidreza: On the probability of facing random breakdowns: a measure of networks’ vulnerability (2016)
- Ma, Yu-Xin; Xu, Jia-Yi; Peng, Di-Chao; Zhang, Ting; Jin, Cheng-Zhe; Qu, Hua-Min; Chen, Wei; Peng, Qun-Sheng: A visual analysis approach for community detection of multi-context mobile social networks (2013) ioport
- Shneiderman, Ben; Dunne, Cody: Interactive network exploration to derive insights: filtering, clustering, grouping, and simplification (2013) ioport
- Yuan, Junqing; Cao, Jinde; Xia, Bila: Arresting strategy based on dynamic criminal networks changing over time (2013)