BigGraphVis: Leveraging Streaming Algorithms and GPU Acceleration for Visualizing Big Graphs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".