  Graph layouts are key to exploring massive graphs. An enormous number of
nodes and edges do not allow network analysis software to produce meaningful
visualization of the pervasive networks. Long computation time, memory and
display limitations encircle the software's ability to explore massive graphs.
This paper introduces BigGraphVis, a new parallel graph visualization method
that uses GPU parallel processing and community detection algorithm to
visualize graph communities. We combine parallelized streaming community
detection algorithm and probabilistic data structure to leverage parallel
processing of Graphics Processing Unit (GPU). To the best of our knowledge,
this is the first attempt to combine the power of streaming algorithms coupled
with GPU computing to tackle big graph visualization challenges. Our method
extracts community information in a few passes on the edge list, and renders
the community structures using the ForceAtlas2 algorithm. Our experiment with
massive real-life graphs indicates that about 70 to 95 percent speedup can be
achieved by visualizing graph communities, and the visualization appears to be
meaningful and reliable. The biggest graph that we examined contains above 3
million nodes and 34 million edges, and the layout computation took about five
minutes. We also observed that the BigGraphVis coloring strategy can be
successfully applied to produce a more informative ForceAtlas2 layout.
