Dwyer makes the distinction between data and knowledge modeling when visualizing a network. This is in contrast to Munzner  who does not make this distinction. He offers knowledge modeling in visualization as a higher-order visual analysis tool and shows how this aspect of the network is underappreciated by the information community. From his perspective, “higher-order network visualizations are externalizations of the analyst’s mental [knowledge] model of insights about complex data.”
In one example, the author designed a “2.5D network view of stock fund ... movement over time.” This allows the analysts to get a mental image on “when money is shifted out of an underperforming industry sector,” and which other sectors in “the fund were the most significant beneficiaries of the manager’s reweighting.” This shows how the network of complex network movements of financial data (as combined with other data types) can provide a higher-level view of the data than a visualization of the raw data itself.
The author keeps the presentation informal with illustrations and examples to show the differences between data view approaches. For those interested in the challenges of network visualization as a higher-order visual analysis tool, this paper is definitely worth reading.