GraphFlow: A Fast and Accurate Distributed Streaming Graph Computation Model
Bibliographic record
Abstract
Streaming graph computation has been widely applied in many fields, e.g., social network analysis and online product recommendation. However, existing streaming graph computation approaches still present limitations on accuracy and efficiency. To improve the accuracy, some distributed systems use the sequential graph update method based on an incremental computation model. However, these systems cannot handle the dynamic graph update concurrently. The speculation-based parallel updating model can parallelize the graph computation, however, it is restricted due to ignoring the original messages when updating a graph. Streaming graph computation usually requires high accuracy and low latency. As such, it is challenging to utilize incremental computation while simultaneously supplying concurrent processing guarantees.To overcome these challenges, in this paper, we first analyze a number of classical graph algorithms and summarize three principles that graph algorithms should satisfy in streaming scenarios. Based on these principles, we propose GraphFlow, a streaming graph computation model. GraphFlow achieves fast and accurate computation by utilizing incremental state update and propagation. To reduce the impact of concurrent update conflicts, GraphFlow provides a fine-grained lock based parallel update strategy. We implement GraphFlow framework and evaluate its performance and concurrent update conflict probability on real-world datasets. Meanwhile, we compare GraphFlow with two existing representative graph processing systems. Experimental results show GraphFlow achieves low latency and outperforms other graph processing systems given large datasets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".