Solana’s transaction network: analysis, insights, and comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it