Solana’s transaction network: analysis, insights, and comparison
Bibliographic record
Abstract
Solana is recognized for its innovative Proof of History consensus mechanism, a cryptographic method that enables validators—participants responsible for verifying transactions—to efficiently record and order events without extensive communication, thus supporting high transaction rates. Despite its high-speed transactions capability, low cost transaction fees and significant market presence, it remains relatively underexplored in academic research. To address this gap, this paper uses graph-based modeling to analyze Solana’s transaction network. The analysis reveals several interesting key characteristics, including a high concentration of transactions among central nodes, a prevalence of unidirectional transactions, and a low graph density. Moreover, we observe a significantly higher transaction failure rate (approximately 20% compared to 0.1% on Ethereum) and a substantial proportion of zero-value transfers (around 7.6% versus 0.66% on Ethereum). These findings shed light on underexplored aspects of Solana’s ecosystem and provide insights that could influence future blockchain research and applications. The findings are particularly relevant for understanding behavior of blockchains with high transaction rates, and optimizing blockchain scalability and security.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".