Authors:
Ehsan Moradi
and
Debajyoti Mondal
Affiliation:
Department of Computer Science, University of Saskatchewan, Canada
Keyword(s):
Graph Visualization, Big Graphs, Community Detection, GPGPU, Streaming Algorithms, Count-Min Sketch.
Abstract:
Graph layouts are key to exploring massive graphs. Motivated by the advances in streaming community detection methods that process the edge list in one pass with only a few operations per edge, we examine whether
they can be leveraged to rapidly create a coarse visualization of the graph communities, and if so, then how
the quality would compare with the layout of the whole graph. We introduce BigGraphVis which combines a
parallelized streaming community detection algorithm and probabilistic data structure to leverage the parallel
processing power of GPUs to visualize graph communities. To the best of our knowledge, this is the first
attempt to combine the potential of streaming algorithms coupled with GPU computing to tackle community
visualization challenges in big graphs. Our method extracts community information in a few passes on the
edge list, and renders the community structures using a widely used ForceAtlas2 algorithm. The coarse layout
generation process of BigGraph
Vis is 70 to 95 percent faster than computing a GPU-accelerated ForceAtlas2
layout of the whole graph. Our experimental results show that BigGraphVis can produce meaningful layouts,
and thus opens up future opportunities to design streaming algorithms that achieve a significant computational
speed up for massive networks by carefully trading off the layout quality.
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