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<p>Abstract</p> <p>Background</p> <p>Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (<url>http://sailing.cs.cmu.edu/tvnviewer</url>), a visualization tool for dynamic network analysis.</p> <p>Results</p> <p>In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.</p> <p>Conclusions</p> <p>TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.</p>