Navigating Contextual Complexity with Graph Visualization
of that flow , and limits placed on where those flows can be delivered . Data systems may or may not have the required contextual consistency that is lacking in the design of many physical systems . Digital thread engineering tends to be fraught with challenges similar to those that are found in physical engineering systems . [ 18 ]
Digital threads are designed to describe and orchestrate data management and machine interaction across the many layers of concern and through multiple translations to new contexts and throughout a product lifecycle . [ 15 ] Every system or subsystem , and every digital thread connecting them , are subgraphs in a System of Systems ( SoS ) that describe the network of information and how it is changing over time . Using graph-based visualization to make the paths between raw data sources , where and how they are transferred and translated to other systems , and audit or trace the paths that data takes , may make it easier to validate digital processes , build trust , and uncover discrepancies .
5 CONCLUSION
Filipov et . al ., asked the question , “ Are We There Yet ?” with respect to the current state of network visualization research and concluded , unequivocally , “ For the last time , no … and stop asking .” [ 6 ] The white space for further research in this area is vast .
The tools with which to manipulate graphs for analytics , display , and interaction are growing in power , capability , and complexity . However , it is clear from reviewing the available research that empirical studies of cognitive and other human aspects of working with ever-more complicated graph-based data environments hasn ’ t kept up with the state-of-the-art of the commercially available technology . It is possible to produce ever more complex visualizations , but there are few empirical measures of their effectiveness with real-world examples for the people who are tasked to use them .
As solution providers , this means that we will continue to expand what we make available based on what application developers , designers , and end users demand from us . As new human factors research is published , we will know more about when and where to use specific techniques . Until then , we will continue to develop new graph-based visual interaction applications , for and with our partners .
6 REFERENCES
[ 1 ] V . Yoghourdjian , D . Archambault , S . Diehl , T . Dwyer , K . Klein , H . Purchase and H . -Y . Wu , " Exploring the Limits of Complexity : A survey of empirical studies on graph visualization ," Visual Informatics , pp . 264-282 , 2018 .
[ 2 ] M . Burch , W . Huang , M . Wakefield , H . C . Purchase , D . Weiskopf and J . Hua , " The State of the Art in Empirical User Evaluation of Graph Visualizations ," vol . 9 , 2021 .
[ 3 ] H . -J . Schultz and H . Schumann , " Visualizing Graphs - A Generalized View ," 2006 .
92 February 2025