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Visualization Symposium, IEEE Pacific (2014)
Yokohama, Japan Japan
Mar. 4, 2014 to Mar. 7, 2014
pp: 105-112
Tim Dwyer , Monash Univ., Melbourne, VIC, Australia
Christopher Mears , Monash Univ., Melbourne, VIC, Australia
Kerri Morgan , Monash Univ., Melbourne, VIC, Australia
Todd Niven , Monash Univ., Melbourne, VIC, Australia
Kim Marriott , Monash Univ., Melbourne, VIC, Australia
Mark Wallace , Monash Univ., Melbourne, VIC, Australia
Drawings of highly connected (dense) graphs can be very difficult to read. Power Graph Analysis offers an alternate way to draw a graph in which sets of nodes with common neighbours are shown grouped into modules. An edge connected to the module then implies a connection to each member of the module. Thus, the entire graph may be represented with much less clutter and without loss of detail. A recent experimental study has shown that such lossless compression of dense graphs makes it easier to follow paths. However, computing optimal power graphs is difficult. In this paper, we show that computing the optimal power-graph with only one module is NP-hard and therefore likely NP-hard in the general case. We give an ILP model for power graph computation and discuss why ILP and CP techniques are poorly suited to the problem. Instead, we are able to find optimal solutions much more quickly using a custom search method. We also show how to restrict this type of search to allow only limited back-tracking to provide a heuristic that has better speed and better results than previously known heuristics.
Programming, Visualization, Rendering (computer graphics), Clutter, Search methods, Image edge detection, Biology,Optimization, Graph Visualisation, Graph Compression
Tim Dwyer, Christopher Mears, Kerri Morgan, Todd Niven, Kim Marriott, Mark Wallace, "Improved Optimal and Approximate Power Graph Compression for Clearer Visualisation of Dense Graphs", Visualization Symposium, IEEE Pacific, vol. 00, no. , pp. 105-112, 2014, doi:10.1109/PacificVis.2014.46
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