BigGraphVis: Leveraging Streaming Algorithms and GPU Acceleration for Visualizing Big Graphs
This addresses the challenge of visualizing big graphs for network analysts, offering a significant speed improvement but is incremental as it builds on existing algorithms like ForceAtlas2.
The paper tackles the problem of visualizing massive graphs by introducing BigGraphVis, a parallel method that uses GPU acceleration and streaming community detection to achieve 70-95% speedup in layout computation, handling graphs with over 3 million nodes and 34 million edges in about five minutes.
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.