A recent data visualization literacy study shows that most people cannot read networks that use hierarchical cluster representations such as "super-noding" and "edge bundling." Other studies that compare standard node-link representations with map-like visualizations show that map-like visualizations are superior in terms of task performance, memorization and engagement  . With this in mind, we propose the Zoomable Multilevel Tree (ZMLT) algorithm for  map-like visualization of large graphs that is representative, real, persistent, overlap-free labeled, planar, and compact. These six desirable properties are formalized with the following guarantees: (1) The abstract and embedded trees represent the underlying graph appropriately at different level of details (in terms of the structure of the graph as well as the embedding thereof); ( 2  ) At every level of detail we show real vertices and real paths from the underlying graph; ( 3 ) If any node or edge appears in a given level, then they also appear in all deeper levels; ( 4 ) All nodes at the current level and higher levels are labeled and there are no label overlaps; ( 5 ) There are no  crossings on any level; ( 6 ) The drawing area is proportional to the total area of the labels. This algorithm is implemented and we have a functional prototype for the interactive interface in a web browser.
Human subject studies that map-like visualizations are as good or better than standard node-link representations of graphs, in terms of task performance, memorization and recall of the underlying data, and engagement SSKB14, SSKB15 . With this in mind, we propose the Zoomable Multi-Level Tree (ZMLT) algorithm for multi-level tree-based, map-like visualization of large graphs . We propose seven desirable properties that such visualization should maintain and an algorithm that accomplishes them. (1) The abstract trees represent the underlying graph appropriately at different level of details ; ( 2 ) The embedded trees represent the underlying graph appropriately at different levels of details; (3 ) At every level of detail we show real vertices and real paths from the underlying graph; ( 4 ) If any node or edge appears in a given level, then they also appear in all deeper levels; ( 5 ) All nodes at the current level and higher levels are labeled and there are no label overlaps; ( 6 ) There are no edge crossings on any level; ( 7 ) The drawing area is proportional to the total area of the labels. This algorithm is implemented and we have a functional prototype for the interactive interface in a web browser.
R
A recent data visualization literacy study shows that most people cannot read networks that use hierarchical cluster representations such as "super-noding" and "edge bundling." Other studies that compare standard node-link representations with
Human subject studies that
R
show that map-like visualizations are superior
are as good or better than standard node-link representations of graphs,
R
engagement
recall of the underlying data, and engagement
A
SSKB14, SSKB15
R
Multilevel
Multi-Level
A
multi-level tree-based,
R
that is representative, real, persistent, overlap-free labeled, planar, and compact. These six desirable properties are formalized with the following guarantees:
. We propose seven desirable properties that such visualization should maintain and an algorithm that accomplishes them.
D
and embedded
R
(in terms of the structure of the graph as well as the embedding thereof); (
; (
A
) The embedded trees represent the underlying graph appropriately at different levels of details; (3
R
3
4
R
4
5
R
5
6
A
edge
R
6
7